{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1zyLCtUdaT3_",
   "metadata": {
    "id": "1zyLCtUdaT3_"
   },
   "source": [
    "# Google Drive connection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "FhDV3eWR995P",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2780,
     "status": "ok",
     "timestamp": 1686345147853,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "FhDV3eWR995P",
    "outputId": "987a2b33-c9d1-4f06-c9a8-2fc2fc87871a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aBKIrzqEaZx3",
   "metadata": {
    "id": "aBKIrzqEaZx3"
   },
   "source": [
    "# Importing Packages and Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b3f3aed1",
   "metadata": {
    "executionInfo": {
     "elapsed": 598,
     "status": "ok",
     "timestamp": 1686345148449,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "b3f3aed1"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from numpy import array\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors\n",
    "import matplotlib.ticker as mtick\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecTTT1gGafpp",
   "metadata": {
    "id": "ecTTT1gGafpp"
   },
   "source": [
    "### Removing Warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f212f3f6",
   "metadata": {
    "executionInfo": {
     "elapsed": 8,
     "status": "ok",
     "timestamp": 1686345148449,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "f212f3f6"
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Liw7_QC1bG8T",
   "metadata": {
    "id": "Liw7_QC1bG8T"
   },
   "source": [
    "### Reading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "qSGTe1JibGeC",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "executionInfo": {
     "elapsed": 440,
     "status": "ok",
     "timestamp": 1686345148881,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "qSGTe1JibGeC",
    "outputId": "88f4c173-6ded-4239-a1ed-94469a3d4348"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>10</td>\n",
       "      <td>101</td>\n",
       "      <td>76</td>\n",
       "      <td>48</td>\n",
       "      <td>180</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.171</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>2</td>\n",
       "      <td>122</td>\n",
       "      <td>70</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>36.8</td>\n",
       "      <td>0.340</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>5</td>\n",
       "      <td>121</td>\n",
       "      <td>72</td>\n",
       "      <td>23</td>\n",
       "      <td>112</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.245</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>1</td>\n",
       "      <td>126</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.349</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>70</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.315</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>768 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0              6      148             72             35        0  33.6   \n",
       "1              1       85             66             29        0  26.6   \n",
       "2              8      183             64              0        0  23.3   \n",
       "3              1       89             66             23       94  28.1   \n",
       "4              0      137             40             35      168  43.1   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "763           10      101             76             48      180  32.9   \n",
       "764            2      122             70             27        0  36.8   \n",
       "765            5      121             72             23      112  26.2   \n",
       "766            1      126             60              0        0  30.1   \n",
       "767            1       93             70             31        0  30.4   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                       0.627   50        1  \n",
       "1                       0.351   31        0  \n",
       "2                       0.672   32        1  \n",
       "3                       0.167   21        0  \n",
       "4                       2.288   33        1  \n",
       "..                        ...  ...      ...  \n",
       "763                     0.171   63        0  \n",
       "764                     0.340   27        0  \n",
       "765                     0.245   30        0  \n",
       "766                     0.349   47        1  \n",
       "767                     0.315   23        0  \n",
       "\n",
       "[768 rows x 9 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv('diabetes.csv')\n",
    "dataset\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b116d154",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Assume that y is a list or array containing the class labels of your dataset\n",
    "unique_labels, counts = np.unique(dataset.Outcome, return_counts=True)\n",
    "\n",
    "plt.bar(unique_labels, counts)\n",
    "plt.xlabel('Class label')\n",
    "plt.ylabel('Number of samples')\n",
    "plt.title('Class distribution in dataset')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6834015",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 15,
     "status": "ok",
     "timestamp": 1686345148882,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "f6834015",
    "outputId": "ae73f458-c00a-4453-ba00-ffdecd8c2d23"
   },
   "outputs": [],
   "source": [
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "886b4280",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "executionInfo": {
     "elapsed": 14,
     "status": "ok",
     "timestamp": 1686345148883,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "886b4280",
    "outputId": "938f3a02-cde8-4055-9697-c890caa53253"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-bc171092-6815-48f5-a47e-543fa8c1401b\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bc171092-6815-48f5-a47e-543fa8c1401b')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-bc171092-6815-48f5-a47e-543fa8c1401b button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-bc171092-6815-48f5-a47e-543fa8c1401b');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hEZJhOQ5bMvB",
   "metadata": {
    "id": "hEZJhOQ5bMvB"
   },
   "source": [
    "### Showing the Attribute Names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0193c2e3",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 13,
     "status": "ok",
     "timestamp": 1686345148883,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "0193c2e3",
    "outputId": "9c0f22e8-9941-478b-800e-553075f83d1a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
       "       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col = dataset.columns\n",
    "col"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9KRF96ifbUNH",
   "metadata": {
    "id": "9KRF96ifbUNH"
   },
   "source": [
    "# Data Analysis and Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b83c57b4",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 11,
     "status": "ok",
     "timestamp": 1686345148884,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "b83c57b4",
    "outputId": "c10e6bd1-cd1b-4227-9d41-849bb40721c4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Pregnancies               768 non-null    int64  \n",
      " 1   Glucose                   768 non-null    int64  \n",
      " 2   BloodPressure             768 non-null    int64  \n",
      " 3   SkinThickness             768 non-null    int64  \n",
      " 4   Insulin                   768 non-null    int64  \n",
      " 5   BMI                       768 non-null    float64\n",
      " 6   DiabetesPedigreeFunction  768 non-null    float64\n",
      " 7   Age                       768 non-null    int64  \n",
      " 8   Outcome                   768 non-null    int64  \n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "FgQXlWkVbYIB",
   "metadata": {
    "id": "FgQXlWkVbYIB"
   },
   "source": [
    "### Checking Null Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b6135290",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 9,
     "status": "ok",
     "timestamp": 1686345148884,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "b6135290",
    "outputId": "f7833f7e-2aac-42ca-84a5-282e945f33e9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                 0\n",
       "Glucose                     0\n",
       "BloodPressure               0\n",
       "SkinThickness               0\n",
       "Insulin                     0\n",
       "BMI                         0\n",
       "DiabetesPedigreeFunction    0\n",
       "Age                         0\n",
       "Outcome                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "IPmOUqksbbeQ",
   "metadata": {
    "id": "IPmOUqksbbeQ"
   },
   "source": [
    "### Ploting Null Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f1b997d8",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 396
    },
    "executionInfo": {
     "elapsed": 672,
     "status": "ok",
     "timestamp": 1686345149549,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "f1b997d8",
    "outputId": "f460cd8d-ff0e-410d-80f5-e141a0c91c08"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABH4AAAF7CAYAAACkQEwNAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy88F64QAAAACXBIWXMAAA9hAAAPYQGoP6dpAABofklEQVR4nO3de5xO5f7/8fc9Z3PEDDPGYZzPhBGGhMIg2zGHSDqRnQjboYkybGWTym7LoSLtEhJKkYw2IuM0mZQmqUgyQ45DNMO4fn/4zf11m3uO3JYZr+fjsR6PmbWua61rrWvd617zmc+6ls0YYwQAAAAAAIAix83qBgAAAAAAAMA1CPwAAAAAAAAUUQR+AAAAAAAAiigCPwAAAAAAAEUUgR8AAAAAAIAiisAPAAAAAABAEUXgBwAAAAAAoIgi8AMAAAAAAFBEEfgBAAAAAAAoogj8AABwExw8eFA2m002m00HDx60rB0VK1aUzWbTwoULLWtDYXKr9BsAAEBBeVjdAADAjZORkaHly5fr008/1bZt23Ts2DGdP39exYsXV/Xq1dWyZUv1799fdevWtbqpN0xiYqI++ugjFS9eXCNGjLC6OQAAAMAthcAPABQR27Zt08CBA/Xjjz/a53l6eiogIEAnTpzQV199pa+++kr/+te/1KNHDy1evFheXl4WtvjGSExM1KRJkxQREXFLB348PT1Vo0YN+89WqVKlinx8fBQUFGRZGwAAAHDzEPgBgCLgk08+Ua9evZSWlqbg4GCNHj1aPXv2VLVq1SRdyQTavXu3li9frtmzZ2vFihU6f/58kQj8FBZly5bVDz/8YHUz9MUXX1jdBAAAANxEBH4AoJDbv3+/HnzwQaWlpal27dr6/PPPVa5cOYcy7u7uaty4sRo3bqwxY8bo0Ucftai1AAAAAG4mBncGgEJuwoQJSk1NlY+Pj1auXJkl6HOtkiVL6qOPPnL6qE9KSorGjBmjOnXqyN/fX35+fqpTp47Gjh2ro0ePOl3fxo0b7YPf5iSzzMaNG3Os/9NPP+nRRx9V+fLl5e3trXLlymnQoEH6/fffna7zkUcekST9+uuv9vVkTrGxsTm26WoPP/ywbDabHn74YUnSwoULFRUVpaCgIJUsWVJt27bVl19+aS9/6dIl/ec//1FkZKQCAwMVFBSkTp066euvv3a6/twGCT58+LBGjhypOnXqyM/PT97e3goPD1dkZKRGjhypnTt3Zqlz6tQpPf/882rUqJECAwPl5eWlsLAw1a9fX0OGDHGa3ZPT4M5X99HZs2c1YcIE1axZU8WKFVNwcLA6d+6s7du353gcjx8/rpEjR6py5cry8fFRmTJl1KtXL/txye48yMnw4cNls9nUqFGjHMudO3dOfn5+stlseu+99+zzL168qLi4OA0fPlyNGzdWmTJl5OXlpdKlSys6OlqLFy+WMSbP7cm0cOFC2Ww2VaxYMdsyeRkcOiMjQwsXLlR0dLRCQ0Pl5eWlUqVKKTo6WkuWLMm2bZcuXdIbb7yh1q1bKyQkRJ6engoODlaNGjXUp08fLViwIN/7BAAAiiADACi0UlJSjJubm5FkHnvsseta18aNG03x4sWNJCPJ+Pr6Gj8/P/vvJUqUMJs3b85Sb8OGDfYyOckss2HDhmzr/+9//zP+/v5GkgkICDAeHh72ZeHh4ebw4cMOdUNDQ01gYKCRZNzc3ExoaKjD9NJLL+V5/wcOHGgkmYEDB9p/9vDwMAEBAfY2eHh4mE8++cT89ddfpn379kaS8fLycjhOvr6+ZteuXVnWf+DAAXuZAwcOOCxLTEw0JUqUsC93d3c3JUqUMDabzT5v4MCBDnV+++03U6FCBftyNzc3U6JECePu7m6f16pVqyztiIiIMJLM22+/nWVZZr3333/fVK1a1UgyPj4+xtfX177M09PTrF271ukx3LdvnwkPD7eX9fb2tvePl5eXWbVqVbbnQU527txpr/fdd99lW27hwoVGkvH39zfnzp2zz7/6HMtsV+Z5ljn16tXLZGRkZFlnTv329ttvG0kmIiIi2zblVN+YK5/hpk2bOrQlKCjI4fcuXbqYtLQ0h3qXLl0y7dq1y1LP29vbYR4AAAAZPwBQiG3YsEGXL1+WJHXv3r3A6/ntt9/UrVs3nT59WrVr19aWLVv0559/6ty5c/ryyy9Vo0YNnTp1Sl27dnWaeXOj9OzZU/fcc4+SkpKUmpqqP//8U0uXLlVAQICOHDmimJgYh/IpKSn697//LUkqX768UlJSHKbRo0fnuw0ff/yxPvjgA82bN0+pqalKTU3VDz/8oMjISF26dEnDhg3T6NGjtWvXLn3wwQc6d+6czp49q127dqlKlSo6f/68nn766Xxt8x//+IdOnTqlRo0aKT4+XhcvXtTJkyf1119/6ccff9SMGTNUp04dhzqxsbE6dOiQKlasqPXr1ys9PV0nT55UWlqaDh48qDlz5qhZs2b53n9JGjp0qLy8vPS///3Pfh7s2LFDNWrU0MWLF/XEE0/Yz7tMFy9e1P33368jR44oJCREK1as0J9//qkzZ84oKSlJd911lwYOHFig9jRu3Fi1a9eWJL377rvZlstc1qNHD/n5+dnnFytWTP369dPq1auVkpKiCxcu6OzZszpx4oT+/e9/KzAwUMuWLdOsWbMK1L6CSk9P19/+9jdt375djRo10urVq/Xnn3/q9OnTOnfunN555x2VLl1aq1at0rhx4xzqLl68WHFxcfLx8dFbb72ls2fP6vTp07pw4YKOHj2qFStWqGfPnjd1fwAAwC3K6sgTAKDgJkyYYP/P/u+//17g9QwZMsSe1ZOcnJxl+W+//WbP3Bg6dKjDshuZ8dOmTRunWRevvfaakWSKFStmLl686LAsL1kXeZGZ5SPJvPfee1mW//zzzw4ZOM6yn7744gv78t9++81hWU6ZH8WKFTOSzNatW/Pc3lq1atmzc/IjLxk/pUqVMkePHs2yfM+ePfYyW7ZscVj27rvvGknGZrOZL7/8MkvdCxcumJo1axYo48cYY6ZOnWokmXLlyjk9Rw4fPmzPflu/fn2+1r1s2TIjyVSpUiXLMldm/MyaNctIMnXq1DGpqalO6+/atcvYbDbj5eXl0Cd///vfjSQzePDgPO8nAAC4PZHxAwCF2IkTJ+w/lyxZskDrMMbogw8+kCQNGTJEYWFhWcqUK1dOQ4YMkSQtWbKkQNvJi2effVZublm/mrp27SpJunDhgvbv3++y7UtShQoV1K9fvyzzK1eurCpVqkiSWrZsqbvuuitLmVatWsnb21uStGfPnjxvs3jx4pKk5ORkl9bJq8GDB6t06dJZ5terV0+VKlWSlHX/li1bJkm6++671bJlyyx1fXx8NGbMmAK36cEHH5Sbm5sOHz6sDRs2ZFm+aNEiXb58WeXKlVObNm3yte777rtPkvTzzz+75Hhm56233pIkPfnkkwoICHBaJjIyUnXq1FF6errDfmf2f0pKisvbCQAACjcCPwBQiJkCDEh7rQMHDujkyZOSpLZt22Zbrl27dpKuBJsOHDhw3dt1pmnTpk7nh4eH23/ObKurNG7cONuBqkNDQyVJd955p9Pl7u7uCgkJkXRl4OW86ty5syRp4MCB+sc//qFNmzbp/PnzearzzDPPaPDgwVq7dq1SU1PzvM2cZNcP0v/1xbX9kDl4c6tWrbKt27p16wK3qVy5cvb6zh73ypzXv39/p8HDs2fP6qWXXlKrVq1UunRpeXl52Qdd9vX1tZdz5aOM17YnM3j23HPPKSwsLNtp3759kq4MYJ6pU6dOstlsWrVqlTp27KjFixfryJEjN6XtAACgcCHwAwCFWGaQQSp4QOTYsWP2n8uWLZttuavfFnZ1nRspu6wHDw8P+88XL150ybZza8PV7chLmfy0c/r06WrTpo3OnTunV155Ra1bt1ZgYKAaN26siRMnOg1GjBkzRr1799bFixf15ptvqmPHjipevLjq1aunMWPG6Mcff8zz9q9VkP37448/JDkG6a6V0/mVFw899JAkafny5Q6BscTERH333XcOZa72448/qnbt2ho7dqy+/PJL/fHHH/L09FSpUqUUGhpqD+hJ0p9//nldbcyrlJQU+zhJJ0+e1NGjR7OdMo/11ft81113adq0afLy8tLatWvVr18/lS1bVuXLl9cjjzziNCsKAADcngj8AEAhdvWAv7t3777u9eX2Svb8lkPeFC9eXP/73/+0efNmjR07Vi1atJCHh4cSEhI0efJkVatWTYsXL3ao4+npqaVLlyoxMVHPP/+87rnnHvn6+uq7777TjBkzVLt2bb388ss3fV9ceW707NlTvr6+OnfunFauXGmfn5ntExkZaR8E+mqPPPKIDh8+rIoVK2rZsmU6ceKE/vzzTx07dkwpKSkOgbUbkUWXFxkZGfaft23bJmNMrlNsbKzDOsaMGaMDBw7o1VdfVbdu3VS6dGkdPnxYCxcu1D333KNevXq5PFAKAABufQR+AKAQa9Omjf2xlqv/EM6Pq8dy+e2337Itd/jwYfvPpUqVsv98dTbOX3/95bTumTNnCtS2201mFseWLVt0+vRpffzxx6pXr54uXLigRx99VEePHs1S54477tCkSZP0xRdf6PTp01q/fr3uvvtuZWRkaMyYMfrmm29uStszz4mcHje63seo/P397W+vywz2ZGRk2INiAwYMyFLnt99+09atWyVdeRPW/fffn2U8rIKOk5N57md33kvZn/tXZxl9++23Bdq+dCXDasSIEVq5cqWOHj2qPXv26PHHH5ckffjhh5ozZ06B1w0AAIoGAj8AUIiFhobaX9n8/vvv5+vxnszMhkqVKtn/EP7iiy+yLb9+/XpJUnBwsH2AX0kqUaKE/efsAkfbt2/Pc7vyKzPwdbMyNW4WHx8fdenSRStWrJB0JbiwZcuWHOt4eHjo3nvv1erVq+Xt7S1jjL3fXK1Ro0aSpI0bN2ZbJqdleZX5KNf69euVkpKi9evXKzk5WR4eHnrggQeylL/6nGzYsKHTdRb0GGWe+8eOHVNaWprTMtmd+yVKlLBnJ93IAdPr1aunN998Uy1atJAkxcXF3bB1AwCAwonADwAUclOmTJG/v78uXLigHj165JpVcerUKfXs2dOeiWCz2dSnTx9J0rx585xmPxw5ckTz5s2TpCx/XFevXl3FihWTdGXslWtdvnxZU6dOzf+O5VFgYKAk6fTp0y7bhitdunTJPtaLM5nHVroyeHSm7AINkuTt7W0ve3UdV7r//vslSV9++aW++uqrLMvT0tI0Y8aM695O27ZtFR4eroyMDC1atMie+dOhQwenbyILCgqy/+ws++ns2bOaMmVKgdpyxx13SLoSdHSWcXfhwgW9+uqr2dYfPHiwpCsB19yCP9eO4ZVT/0v/d97crP4HAAC3LgI/AFDIVa9eXe+++668vLy0d+9eNWjQQNOmTdNPP/1kL5ORkaHdu3fr+eefV+XKle1ZJJmeffZZFS9eXCdPnlTbtm3tj8ZI0ldffaW2bdvq9OnTKlmypJ555hmHup6envasoxdffFEffPCB0tPTJUn79u1T9+7dXfq4Ud26dSVJqamp9tfSFyaHDx9WtWrVNGXKFO3evVuXLl2yL9uzZ48efPBBSZKfn5/uvvtu+7KIiAjFxMRo27ZtDkGAn376Sf3799f58+fl5uam6Ojom7Ifffr0UZ06dWSMUY8ePfTxxx/bx7HZt2+fOnfufENePe7m5qZ+/fpJkubPn6+PPvpIkvPHvCSpdu3aqlChgiTp0UcfVUJCgn1ZfHy8Wrduna83sF2tXLlyuuuuuyRJo0aN0vr16+37nJCQoLZt2+Y4EPqQIUPsb1AbMGCAJkyY4JChdP78eW3cuFFPPfWUqlSp4lC3W7duevTRR/XZZ585BD1PnjypKVOm2LP3OnXqVKB9AwAARYgBABQJW7ZsMVWrVjWS7JOXl5cpWbKkcXNzs8+z2WzmgQceMOnp6Q71N27caIKCguzl/Pz8jJ+fn/334sWLmy+//NLptn/77TcTHh5uL+vp6WkCAwONJBMQEGA2btxoX7ZhwwaHuhs2bLAvy0l29Y0x5t5777UvDwgIMBERESYiIsK8+uqreT5+AwcONJLMwIEDsy3TqlUrI8lMnDgx2zIRERFGknn77bcd5h84cMDexgMHDjidL8m4u7ubkiVLGi8vL4d+XLZsmcP6rq7j5uZmSpQoYXx8fBz62dn+Z9e+q9fp7Bjn5RgkJSWZsLAw+3q8vb3t55S3t7f55JNP7Mvi4+Oz3UZuvv32W4f9DwoKMhcuXMi2/CeffGI8PDzs5X19fY2vr6/95/Xr12e779n1W6bdu3ebgIAAexkfHx/75yY0NNSsXr06x/p//PGHueeeexz2JzAw0BQvXtzYbDb7PA8PD4d6mf1wdZ3Mz1zmdP/995uMjIyCHGIAAFCEkPEDAEVEixYt9MMPP2jx4sXq37+/qlatKh8fH509e1YlS5bUXXfdpfHjxyspKUnvv/++PD09Heq3atVKP/zwg/7xj3+oVq1aunz5sowxqlWrlkaPHq2kpCS1bNnS6bbLlSun7du36/HHH7e/stvf318PPfSQvv76a7Vq1cql+/7hhx9q5MiRql69ui5evKhff/1Vv/76a6F4/Kts2bJatWqVRo4cqWbNmqlMmTI6d+6cPDw8VLt2bQ0dOlTfffed/VGqTOvWrVNMTIxatmyp8uXL68KFC5KkqlWr6pFHHtHOnTs1YsSIm7ovNWvW1J49ezR8+HBVrFhRxhj5+Piod+/e2rZtm33cGenKm8wKqm7dumrQoIH99969e8vHxyfb8p07d9aXX36p++67T8WLF9elS5cUEhKiRx55RF9//bXuvffeArelQYMG2rFjh/r27avSpUvr8uXLCgkJ0dChQ5WYmOj0LWNXCwkJ0fr16/Xxxx/r/vvvV/ny5ZWWlqYLFy6obNmy6tixo2bNmqWDBw861PvPf/6jadOmqVOnTqpWrZqMMbpw4YLCw8PVpUsXLV++XMuWLbOPgQUAAG5fNmOK2GiYAADglhQXF6f27dvL29tbZ8+ezRJ8BAAAwI3Hv4EAAIDLGWM0bdo0SdK9995L0AcAAOAmIfADAABuiA0bNmjEiBHatWuX/dEzY4wSEhL0t7/9TV988YVsNpvGjh1rcUsBAABuHzzqBQAAboiPPvpI3bt3t/9eokQJXbhwQX/99ZckyWazacaMGRo1apRVTQQAALjtkPEDAABuiGbNmumf//ynWrdurQoVKtgDPpUrV9bAgQO1Y8cOgj4AAOCWMXv2bFWqVEk+Pj6KjIzU5s2bsy2bnJysfv36qUaNGnJzc8v2JRrLly9X7dq15e3trdq1a2vlypXXtd0bgcAPAAC4IcLCwjRhwgRt2LBBv/76q86fP68LFy7o559/1sKFC9W4cWOrmwgAACBJWrp0qUaMGKHx48dr9+7datmypTp27KhDhw45LZ+WlqZSpUpp/PjxuuOOO5yWiY+PV58+fTRgwAB98803GjBggHr37q3t27cXeLs3Ao96AQAAAACA20rTpk3VqFEjzZkzxz6vVq1a6tatm6ZOnZpj3datW6tBgwaaOXOmw/w+ffooNTVVn332mX1ehw4dVKJECS1evPi6t1tQZPwAAAAAAIBCLS0tTampqQ5TWlqa07Lp6elKSEhQ+/btHea3b99eW7duLXAb4uPjs6wzOjravk5XbTc3HnktaLTBZY0AAAAAAABX2NTG6iZYwvb3ZgWuOzG0gyZNmuQ4b+JExcbGZil7/PhxZWRkKDQ01GF+aGioUlJSCtyGlJSUHNfpqu3mJs+BHwAAAAAAAFexudkKXDcmJibLSyS8vb1z3p7NcXvGmCzz8isv63TFdnNC4AcAAAAAABRq3t7euQZ6MoWEhMjd3T1Lls2xY8eyZOPkR1hYWI7rdNV2c8MYPwAAAAAAwHI2N1uBp/zw8vJSZGSk4uLiHObHxcWpefPmBW5/VFRUlnWuW7fOvk5XbTc3ZPwAAAAAAADLXc+jXvk1atQoDRgwQI0bN1ZUVJTeeOMNHTp0SEOGDJF05dGx33//Xf/973/tdRITEyVJ586d0x9//KHExER5eXmpdu3akqSnn35ad999t6ZNm6auXbvq448/1vr167Vly5Y8b9cVCPwAAAAAAADL3czAT58+fXTixAlNnjxZycnJqlu3rtasWaOIiAhJUnJysg4dOuRQp2HDhvafExIS9P777ysiIkIHDx6UJDVv3lxLlizRhAkT9Nxzz6lKlSpaunSpmjZtmuftuoLNGGPyUpC3egEAAAAA4Hq361u9vEa1LHDd9Fc238CWFC1k/AAAAAAAAMvdzIyf2wmDOwMAAAAAABRRZPwAAAAAAADLkfHjGgR+AAAAAACA5Qj8uAaBHwAAAAAAYDkCP65B4AcAAAAAAFiOwI9rMLgzAAAAAABAEUXGDwAAAAAAsBwZP65B4AcAAAAAAFiOwI9rEPgBAAAAAACWs9kI/LgCgR8AAAAAAGA5Mn5cg8APAAAAAACwHIEf1+CtXgAAAAAAAEUUGT8AAAAAAMByZPy4BoEfAAAAAABgOQI/rkHgBwAAAAAAWI7Aj2sQ+AEAAAAAAJYj8OMaBH4AAAAAAIDlCPy4Bm/1AgAAAAAAKKLI+AEAAAAAAJYj48c1CPwAAAAAAADL2WwEflyBwA8AAAAAALAcGT+uQeAHAAAAAABYjsCPaxD4AQAAAAAAliPw4xq81QsAAAAAAKCIIuMHAAAAAABYzo3UFJcg8AMAAAAAACznzlu9XIJ4GgAAAAAAsJy7m63AU0HMnj1blSpVko+PjyIjI7V58+Ycy2/atEmRkZHy8fFR5cqVNXfuXIflrVu3ls1myzLdd9999jKxsbFZloeFhRWo/XlFxg8AAAAAALDczcz4Wbp0qUaMGKHZs2erRYsWmjdvnjp27Kjvv/9eFSpUyFL+wIED6tSpkwYNGqT33ntPX331lZ588kmVKlVKPXv2lCStWLFC6enp9jonTpzQHXfcoV69ejmsq06dOlq/fr39d3d3dxft5RUEfgAAAAAAgOXcb+IzSa+88ooee+wxPf7445KkmTNn6vPPP9ecOXM0derULOXnzp2rChUqaObMmZKkWrVqadeuXZoxY4Y98FOyZEmHOkuWLJGvr2+WwI+Hh4fLs3yuxqNeAAAAAADgtpGenq6EhAS1b9/eYX779u21detWp3Xi4+OzlI+OjtauXbt08eJFp3Xmz5+vvn37ys/Pz2H+/v37FR4erkqVKqlv37765ZdfrmNvckfGDwAAAAAAsNz1POqVlpamtLQ0h3ne3t7y9vbOUvb48ePKyMhQaGiow/zQ0FClpKQ4XX9KSorT8pcuXdLx48dVpkwZh2U7duzQd999p/nz5zvMb9q0qf773/+qevXqOnr0qKZMmaLmzZtr7969Cg4OzvP+5gcZPwAAAAAAwHLuNluBp6lTpyooKMhhcvbI1tVs1wSajDFZ5uVW3tl86Uq2T926ddWkSROH+R07dlTPnj1Vr149tW3bVqtXr5YkvfPOOzm29XqQ8QMAAAAAACxX0LdzSVJMTIxGjRrlMM9Zto8khYSEyN3dPUt2z7Fjx7Jk9WQKCwtzWt7DwyNLps758+e1ZMkSTZ48Odd2+/n5qV69etq/f3+uZQuKjB8AAAAAAGA5d1vBJ29vbwUGBjpM2QV+vLy8FBkZqbi4OIf5cXFxat68udM6UVFRWcqvW7dOjRs3lqenp8P8Dz74QGlpaXrwwQdz3ee0tDQlJSVleVTsRiLwAwAAAAAALOfuZivwlF+jRo3SW2+9pQULFigpKUkjR47UoUOHNGTIEElXMogeeughe/khQ4bo119/1ahRo5SUlKQFCxZo/vz5Gj16dJZ1z58/X926dXM6Zs/o0aO1adMmHThwQNu3b9f999+v1NRUDRw4MN/7kFc86gUAAAAAAG4rffr00YkTJzR58mQlJyerbt26WrNmjSIiIiRJycnJOnTokL18pUqVtGbNGo0cOVKvv/66wsPD9dprr9lf5Z7pxx9/1JYtW7Ru3Tqn2z18+LAeeOABHT9+XKVKlVKzZs20bds2+3ZdwWYyRyPKhdEGlzUCAAAAAABcYVMbq5tgieZLHihw3a19F9/AlhQtZPwAAAAAAADLXc/gzsgegR8AAAAAAGA5d+I+LkHgBwAAAAAAWI6MH9cg8AMAAAAAACznbiPw4wq8zh0AAAAAAKCIIuMHAAAAAABYjowf1yDwAwAAAAAALOfOM0kuQeAHAAAAAABYjowf1yDwAwAAAAAALMdbvVyDwA8AAAAAALAcGT+uwRN0AAAAAAAARRQZPwAAAAAAwHIM7uwaBH4AAAAAAIDleNTLNQj8AAAAAAAAyzG4s2sQ+AEAAAAAAJYj48c1CPwAAAAAAADLMcaPa3BYAQAAAAAAiigyfgAAAAAAgOV41Ms1CPwAAAAAAADLuRP3cQkCPwAAAAAAwHJuZPy4BIEfAAAAAABgOTJ+XIPADwAAAAAAsJwbgR+X4K1eAAAAAAAARRQZPwAAAAAAwHI86uUaBH4AAAAAAIDl3HjWyyUI/AAAAAAAAMuR8eMaBH4AAAAAAIDlSPhxDQI/AAAAAADAcmT8uAZv9QIAAAAAALed2bNnq1KlSvLx8VFkZKQ2b96cY/lNmzYpMjJSPj4+qly5subOneuwfOHChbLZbFmmv/7667q2e70I/AAAAAAAAMu52WwFnvJr6dKlGjFihMaPH6/du3erZcuW6tixow4dOuS0/IEDB9SpUye1bNlSu3fv1rPPPqvhw4dr+fLlDuUCAwOVnJzsMPn4+BR4uzeCzRhj8lLQaIPLGgEAAAAAAK6wqY3VTbDEP3c8UeC6zzWZl6/yTZs2VaNGjTRnzhz7vFq1aqlbt26aOnVqlvLjxo3TqlWrlJSUZJ83ZMgQffPNN4qPj5d0JeNnxIgROn369A3b7o1Axg8AAAAAALCcm63gU1pamlJTUx2mtLQ0p9tJT09XQkKC2rdv7zC/ffv22rp1q9M68fHxWcpHR0dr165dunjxon3euXPnFBERoXLlyqlz587avXv3dW33RiDwAwAAAAAALOdusxV4mjp1qoKCghym7DJojh8/royMDIWGhjrMDw0NVUpKitM6KSkpTstfunRJx48flyTVrFlTCxcu1KpVq7R48WL5+PioRYsW2r9/f4G3eyPwVi8AAAAAAGC563mde0xMjEaNGuUwz9vbO8c6tmvGBjLGZJmXW/mr5zdr1kzNmjWzL2/RooUaNWqk//znP3rttdcKvN3rReAHAAAAAAAUat7e3rkGejKFhITI3d09S5bNsWPHsmTjZAoLC3Na3sPDQ8HBwU7ruLm56c4777Rn/BRkuzcCj3oBAAAAAADLudsKPuWHl5eXIiMjFRcX5zA/Li5OzZs3d1onKioqS/l169apcePG8vT0dFrHGKPExESVKVOmwNu9Ecj4AQAAAAAAlnO7iakpo0aN0oABA9S4cWNFRUXpjTfe0KFDhzRkyBBJVx4d+/333/Xf//5X0pU3eM2aNUujRo3SoEGDFB8fr/nz52vx4sX2dU6aNEnNmjVTtWrVlJqaqtdee02JiYl6/fXX87xdVyDwAwAAAAAALOfuwnFurtWnTx+dOHFCkydPVnJysurWras1a9YoIiJCkpScnKxDhw7Zy1eqVElr1qzRyJEj9frrrys8PFyvvfaaevbsaS9z+vRpDR48WCkpKQoKClLDhg315ZdfqkmTJnnerivYTOZoRLkw2uCyRgAAAAAAgCtsamN1Eywxa8/fC1z3qfpzbmBLihYyfgAAAAAAgOXyO1YP8obBnQEAAAAAAIooMn4AAAAAAIDl3Mj4cQkCPwAAAAAAwHI3c3Dn2wmBHwAAAAAAYDkyflyDwA8AAAAAALAcgzu7BoM7AwAAAAAAFFFk/AAAAAAAAMu5McaPSxD4AQAAAAAAluNRL9cg8AMAAAAAACxHxo9rEPgBAAAAAACWI/DjGgR+AAAAAACA5Qj8uAZv9QIAAAAAACiiyPgBAAAAAACWc7ORm+IKBH4AAAAAAIDleNTLNQj8AAAAAAAAyxH4cQ0CPwAAAAAAwHIEflyDwA8AAAAAALCcG++fcgmOKgAAAAAAQBFFxg8AAAAAALAcj3q5BoEfAAAAAABgOQI/rkHgBwAAAAAAWM7Nxmg0rkDgBwAAAAAAWI6MH9cg8AMAAAAAACxH4Mc1yKMCAAAAAAAoosj4AQAAAAAAliPjxzUI/AAAAAAAAMsxuLNrcFQBAAAAAIDl3GQr8FQQs2fPVqVKleTj46PIyEht3rw5x/KbNm1SZGSkfHx8VLlyZc2dO9dh+ZtvvqmWLVuqRIkSKlGihNq2basdO3Y4lImNjZXNZnOYwsLCCtT+vCLwAwAAAAAALOdmsxV4yq+lS5dqxIgRGj9+vHbv3q2WLVuqY8eOOnTokNPyBw4cUKdOndSyZUvt3r1bzz77rIYPH67ly5fby2zcuFEPPPCANmzYoPj4eFWoUEHt27fX77//7rCuOnXqKDk52T59++23+W5/ftiMMSYvBY02uLQhAAAAAABAsqmN1U2wxI6jkwtct0no8/kq37RpUzVq1Ehz5syxz6tVq5a6deumqVOnZik/btw4rVq1SklJSfZ5Q4YM0TfffKP4+Hin28jIyFCJEiU0a9YsPfTQQ5KuZPx89NFHSkxMzFd7rwcZPwAAAAAA4LaRnp6uhIQEtW/f3mF++/bttXXrVqd14uPjs5SPjo7Wrl27dPHiRad1zp8/r4sXL6pkyZIO8/fv36/w8HBVqlRJffv21S+//HIde5M7BncGAAAAAACWu563eqWlpSktLc1hnre3t7y9vbOUPX78uDIyMhQaGuowPzQ0VCkpKU7Xn5KS4rT8pUuXdPz4cZUpUyZLnWeeeUZly5ZV27Zt7fOaNm2q//73v6pevbqOHj2qKVOmqHnz5tq7d6+Cg4PzvL/5QcYPAAAAAACw3PWM8TN16lQFBQU5TM4e2bqa7ZpAkzEmy7zcyjubL0nTp0/X4sWLtWLFCvn4+Njnd+zYUT179lS9evXUtm1brV69WpL0zjvv5HxwrgMZPwAAAAAAwHLX8zr3mJgYjRo1ymGes2wfSQoJCZG7u3uW7J5jx45lyerJFBYW5rS8h4dHlkydGTNm6MUXX9T69etVv379HNvt5+enevXqaf/+/TmWux5k/AAAAAAAAMtdT8aPt7e3AgMDHabsAj9eXl6KjIxUXFycw/y4uDg1b97caZ2oqKgs5detW6fGjRvL09PTPu+ll17SP//5T61du1aNGzfOdZ/T0tKUlJTk9FGxG4XADwAAAAAAsJybbAWe8mvUqFF66623tGDBAiUlJWnkyJE6dOiQhgwZIulKBlHmm7ikK2/w+vXXXzVq1CglJSVpwYIFmj9/vkaPHm0vM336dE2YMEELFixQxYoVlZKSopSUFJ07d85eZvTo0dq0aZMOHDig7du36/7771dqaqoGDhx4HUcuZzzqBQAAAAAAbit9+vTRiRMnNHnyZCUnJ6tu3bpas2aNIiIiJEnJyck6dOiQvXylSpW0Zs0ajRw5Uq+//rrCw8P12muvqWfPnvYys2fPVnp6uu6//36HbU2cOFGxsbGSpMOHD+uBBx7Q8ePHVapUKTVr1kzbtm2zb9cVbCZzNKJcGG1wWSMAAAAAAMAVNrWxugmW+P7ktALXrV1y3A1sSdFCxg8AAAAAALDc9QzujOwR+AEAAAAAAJZzy+FV6ig4Aj8AAAAAAMByNjJ+XILADwAAAAAAsJwbLx53CY4qAAAAAABAEUXGDwAAAAAAsByPerkGgR8AAAAAAGA53urlGgR+AAAAAACA5WyMRuMSBH4AAAAAAIDlyPhxDQI/AAAAAADAcmT8uAZHFQAAAAAAoIgi4wcAAAAAAFiOR71cg8APAAAAAACwHK9zdw0CPwAAAAAAwHJujEbjEgR+AAAAAACA5cj4cQ0CPwAAAAAAwHKM8eMaHFUAAAAAAIAiiowfAAAAAABgOZvcrW5CkUTgBwAAAAAAWI5HvVyDwA8AAAAAALCcjdFoXILADwAAAAAAsBwZP65B4AcAAAAAAFiO17m7BkcVAAAAAACgiCLjBwAAAAAAWM6N3BSXIPADAAAAAAAsx6NerkHgBwAAAAAAWI7BnV3jtj+q7y/aqHvvGa/69Z5Sjx4vateu/VY3CXlAvxVO9FvhRL8VTvRb4UXfFU70W+FEvxVO9FvRZZNbgSdk77Y+OmvW7NLUqcs05O8dtfKj8WocWVWDB83SkSMnrW4ackC/FU70W+FEvxVO9FvhRd8VTvRb4US/FU70W9HmZnMr8ITs3dZHZ+Hb69WzZwv16nWXqlQpo2fH91ZYWAktXrzJ6qYhB/Rb4US/FU70W+FEvxVe9F3hRL8VTvRb4US/4UaaPXu2KlWqJB8fH0VGRmrz5s05lt+0aZMiIyPl4+OjypUra+7cuVnKLF++XLVr15a3t7dq166tlStXXvd2r9dtG/hJT7+kvXsPqcVdtRzmt2hRS7t3/2JRq5Ab+q1wot8KJ/qtcKLfCi/6rnCi3won+q1wot+Kvpv5qNfSpUs1YsQIjR8/Xrt371bLli3VsWNHHTp0yGn5AwcOqFOnTmrZsqV2796tZ599VsOHD9fy5cvtZeLj49WnTx8NGDBA33zzjQYMGKDevXtr+/btBd7ujXDbBn5OnTqnjIzLCg4OdJgfHBKo43+kWtQq5IZ+K5zot8KJfiuc6LfCi74rnOi3wol+K5zot6LvZj7q9corr+ixxx7T448/rlq1amnmzJkqX7685syZ47T83LlzVaFCBc2cOVO1atXS448/rkcffVQzZsywl5k5c6batWunmJgY1axZUzExMbr33ns1c+bMAm/3RnB6dNLS0pSamuowpaWlu6wRVrLZbI4zjNG1s3Drod8KJ/qtcKLfCif6rfCi7won+q1wot8KJ/qt6LLZ3Ao8OY9jpDndTnp6uhISEtS+fXuH+e3bt9fWrVud1omPj89SPjo6Wrt27dLFixdzLJO5zoJs90ZwGviZOnWqgoKCHKapU993WSOsUKKEv9zd3XT8+BmH+SdOnFVwSGA2tWA1+q1wot8KJ/qtcKLfCi/6rnCi3won+q1wot+KPpsp+OQ8jjHV6XaOHz+ujIwMhYaGOswPDQ1VSkqK0zopKSlOy1+6dEnHjx/PsUzmOguy3RvBaeAnJiZGZ86ccZhiYvq5rBFW8PLyUJ06FbT1qySH+Vu3Jqlhw8oWtQq5od8KJ/qtcKLfCif6rfCi7won+q1wot8KJ/rtNmAuF3hyHseIyXFz12aPGWOyZpTlUv7a+XlZZ363e708nM309vaWt7e3Y0Pk5bJGWOXhR9pq3Ni3VbduhBo0rKwPlm5WcvIp9e17t9VNQw7ot8KJfiuc6LfCiX4rvOi7wol+K5zot8KJfkN2nMUxshMSEiJ3d/csWTbHjh3Lko2TKSwszGl5Dw8PBQcH51gmc50F2e6N4DTwc7vo1KmxTp86p9dnr9Yfx1JVrXq45r3xlMqWDba6acgB/VY40W+FE/1WONFvhRd9VzjRb4UT/VY40W9FnLl8Uzbj5eWlyMhIxcXFqXv37vb5cXFx6tq1q9M6UVFR+uSTTxzmrVu3To0bN5anp6e9TFxcnEaOHOlQpnnz5gXe7o1gM5m5Sbkw2uCyRgAAAAAAgCtsamN1E6xx6fOC1/WIzlfxpUuXasCAAZo7d66ioqL0xhtv6M0339TevXsVERGhmJgY/f777/rvf/8r6crr3OvWrasnnnhCgwYNUnx8vIYMGaLFixerZ8+ekqStW7fq7rvv1gsvvKCuXbvq448/1oQJE7RlyxY1bdo0T9t1hds64wcAAAAAANwiblLGjyT16dNHJ06c0OTJk5WcnKy6detqzZo19uBLcnKyDh06ZC9fqVIlrVmzRiNHjtTrr7+u8PBwvfbaa/agjyQ1b95cS5Ys0YQJE/Tcc8+pSpUqWrp0qT3ok5ftugIZPwAAAAAA3EJu24yftNUFr+t9341rRxFDxg8AAAAAALDeTcz4uZ04fZ07AAAAAAAACj8yfgAAAAAAgPXI+HEJAj8AAAAAAMB6BH5cgsAPAAAAAACw3mUCP65A4AcAAAAAAFiPjB+XYHBnAAAAAACAIoqMHwAAAAAAYD0yflyCwA8AAAAAALAegR+XIPADAAAAAAAsZ0xGgevabmA7ihoCPwAAAAAAwHq81cslCPwAAAAAAADr8aiXS/BWLwAAAAAAgCKKjB8AAAAAAGA9Mn5cgsAPAAAAAACwHoEflyDwAwAAAAAArEfgxyUI/AAAAAAAAOvxVi+XIPADAAAAAACsR8aPS/BWLwAAAAAAgCKKjB8AAAAAAGA9Mn5cgsAPAAAAAACwHoEflyDwAwAAAAAArMfgzi5B4AcAAAAAAFiPjB+XIPADAAAAAACsR+DHJXirFwAAAAAAQBFFxg8AAAAAALAeY/y4BIEfAAAAAABgvcvG6hYUSTzqBQAAAAAArHf5csEnFzp16pQGDBigoKAgBQUFacCAATp9+nSOdYwxio2NVXh4uIoVK6bWrVtr79699uUnT57UsGHDVKNGDfn6+qpChQoaPny4zpw547CeihUrymazOUzPPPNMvtpP4AcAAAAAAFjvFg389OvXT4mJiVq7dq3Wrl2rxMREDRgwIMc606dP1yuvvKJZs2Zp586dCgsLU7t27XT27FlJ0pEjR3TkyBHNmDFD3377rRYuXKi1a9fqsccey7KuyZMnKzk52T5NmDAhX+23GWPylEtltCFfKwYAAAAAAPlnUxurm2AJkzSlwHVttfIXDMmrpKQk1a5dW9u2bVPTpk0lSdu2bVNUVJR++OEH1ahRI0sdY4zCw8M1YsQIjRs3TpKUlpam0NBQTZs2TU888YTTbS1btkwPPvig/vzzT3l4XBmZp2LFihoxYoRGjBhR4H0g4wcAAAAAAMCJ+Ph4BQUF2YM+ktSsWTMFBQVp69atTuscOHBAKSkpat++vX2et7e3WrVqlW0dSTpz5owCAwPtQZ9M06ZNU3BwsBo0aKAXXnhB6enp+doHBncGAAAAAADWu45HttLS0pSWluYwz9vbW97e3tfVpJSUFJUuXTrL/NKlSyslJSXbOpIUGhrqMD80NFS//vqr0zonTpzQP//5zyzZQE8//bQaNWqkEiVKaMeOHYqJidGBAwf01ltv5XkfyPgBAAAAAADWu44xfqZOnWoffDlzmjp1arabio2NzTJo8rXTrl27JEk2my1LfWOM0/lXu3Z5dnVSU1N13333qXbt2po4caLDspEjR6pVq1aqX7++Hn/8cc2dO1fz58/XiRMnctz21cj4AQAAAAAA1ruO17nHxDyrUaNGOczLKdvnqaeeUt++fXNcZ8WKFbVnzx4dPXo0y7I//vgjS0ZPprCwMElXMn/KlCljn3/s2LEsdc6ePasOHTrI399fK1eulKenZ45tatasmSTpp59+UnBwcI5lMxH4AQAAAAAA1ruOR73y+1hXSEiIQkJCci0XFRWlM2fOaMeOHWrSpIkkafv27Tpz5oyaN2/utE6lSpUUFhamuLg4NWzYUJKUnp6uTZs2adq0afZyqampio6Olre3t1atWiUfH59c27N7925Jcggo5YbADwAAAAAAsN51ZPy4Sq1atdShQwcNGjRI8+bNkyQNHjxYnTt3dnijV82aNTV16lR1795dNptNI0aM0Isvvqhq1aqpWrVqevHFF+Xr66t+/fpJupLp0759e50/f17vvfeeUlNTlZqaKkkqVaqU3N3dFR8fr23btqlNmzYKCgrSzp07NXLkSHXp0kUVKlTI8z4Q+AEAAAAAAMjGokWLNHz4cPtburp06aJZs2Y5lNm3b5/OnDlj/33s2LG6cOGCnnzySZ06dUpNmzbVunXrFBAQIElKSEjQ9u3bJUlVq1Z1WNeBAwdUsWJFeXt7a+nSpZo0aZLS0tIUERGhQYMGaezYsflqv80Yk6eQmtGGfK0YAAAAAADkn01trG6CJczOZwtc13bnizewJUULGT8AAAAAAMB61zHGD7JH4AcAAAAAAFgujw8kOZXzi9VvbwR+AAAAAACA9cj4cQkCPwAAAAAAwHoEflzCzeoGAAAAAAAAwDXI+AEAAAAAANa7XPAxfpA9Aj8AAAAAAMB6POrlEgR+AAAAAACA9Qj8uASBHwAAAAAAYD0e9XIJAj8AAAAAAMB6ZPy4BG/1AgAAAAAAKKLI+AEAAAAAANYj48clCPwAAAAAAADrMcaPSxD4AQAAAAAA1iPjxyUI/AAAAAAAAOsR+HEJAj8AAAAAAMB6POrlErzVCwAAAAAAoIgi4wcAAAAAAFiPR71cgsAPAAAAAACwnMngUS9XIPADAAAAAACsxxg/LkHgBwAAAAAAWI+MH5cg8AMAAAAAACxnyPhxCd7qBQAAAAAAUESR8QMAAAAAAKzHo14uQeAHAAAAAABYL4PXubsCgR8AAAAAAGA5xvhxDQI/AAAAAADAejzq5RIEfgAAAAAAgPXI+HEJ3uoFAAAAAACQjVOnTmnAgAEKCgpSUFCQBgwYoNOnT+dYxxij2NhYhYeHq1ixYmrdurX27t3rUKZ169ay2WwOU9++fa9729ci8AMAAAAAACxnMkyBJ1fq16+fEhMTtXbtWq1du1aJiYkaMGBAjnWmT5+uV155RbNmzdLOnTsVFhamdu3a6ezZsw7lBg0apOTkZPs0b9686972tXjUCwAAAAAAWO/yrfdWr6SkJK1du1bbtm1T06ZNJUlvvvmmoqKitG/fPtWoUSNLHWOMZs6cqfHjx6tHjx6SpHfeeUehoaF6//339cQTT9jL+vr6Kiws7IZt2xkyfgAAAAAAgPUyTIGntLQ0paamOkxpaWnX3aT4+HgFBQXZAy+S1KxZMwUFBWnr1q1O6xw4cEApKSlq3769fZ63t7datWqVpc6iRYsUEhKiOnXqaPTo0Q4ZQQXZtjMEfgAAAAAAgOXMZVPgaerUqfZxcDKnqVOnXnebUlJSVLp06SzzS5curZSUlGzrSFJoaKjD/NDQUIc6/fv31+LFi7Vx40Y999xzWr58uT1DqKDbdoZHvQAAAAAAgPWuY6yemJgYjRo1ymGet7d3tuVjY2M1adKkHNe5c+dOSZLNZsuyzBjjdP7Vrl1+bZ1BgwbZf65bt66qVaumxo0b6+uvv1ajRo2ua9tXI/ADAAAAAAAKNW9v7xwDPdd66qmnsrxB61oVK1bUnj17dPTo0SzL/vjjjywZPZkyx+xJSUlRmTJl7POPHTuWbR1JatSokTw9PbV//341atRIYWFh+d62MwR+AAAAAACA9Vz8dq6rhYSEKCQkJNdyUVFROnPmjHbs2KEmTZpIkrZv364zZ86oefPmTutUqlRJYWFhiouLU8OGDSVJ6enp2rRpk6ZNm5bttvbu3auLFy/ag0UF2bYzjPEDAAAAAAAsdz1j/LhKrVq11KFDBw0aNEjbtm3Ttm3bNGjQIHXu3NnhrVo1a9bUypUrJV15PGvEiBF68cUXtXLlSn333Xd6+OGH5evrq379+kmSfv75Z02ePFm7du3SwYMHtWbNGvXq1UsNGzZUixYt8rXt3JDxAwAAAAAArJdx673OXbry5q3hw4fb39LVpUsXzZo1y6HMvn37dObMGfvvY8eO1YULF/Tkk0/q1KlTatq0qdatW6eAgABJkpeXl7744gv9+9//1rlz51S+fHndd999mjhxotzd3fO17dzYjDF5Co0ZbcjXigEAAAAAQP7Z1MbqJljiQmznAtctFvvpDWxJ0ULGDwAAAAAAsN5NHOPndsIYPwAAAAAAAEUUGT8AAAAAAMB6Lhyk+XZG4AcAAAAAAFjO8KiXSxD4AQAAAAAA1iPjxyUI/AAAAAAAAOvdoq9zL+wY3BkAAAAAAKCIIuMHAAAAAABYzvCol0sQ+AEAAAAAANZjcGeXIPADAAAAAAAsR8aPaxD4AQAAAAAAluN17q5B4AcAAAAAAFiOjB/X4K1eAAAAAAAARRQZPwAAAAAAwHKXedTLJQj8AAAAAAAAy/Gol2sQ+AEAAAAAAJYzly9b3YQiicAPAAAAAACwHG/1cg0CPwAAAAAAwHI86uUavNULAAAAAACgiCLjBwAAAAAAWI5HvVyDwA8AAAAAALAcj3q5BoEfAAAAAABgucsEflyCwA8AAAAAALAcj3q5BoEfAAAAAABgOR71cg3e6gUAAAAAAFBEkfEDAAAAAAAsR8aPaxD4AQAAAAAAlmOMH9fgUS8AAAAAAGA5c/lygSdXOnXqlAYMGKCgoCAFBQVpwIABOn36dM77YoxiY2MVHh6uYsWKqXXr1tq7d699+cGDB2Wz2ZxOy5Yts5erWLFiluXPPPNMvtpP4AcAAAAAAFjOZJgCT67Ur18/JSYmau3atVq7dq0SExM1YMCAHOtMnz5dr7zyimbNmqWdO3cqLCxM7dq109mzZyVJ5cuXV3JyssM0adIk+fn5qWPHjg7rmjx5skO5CRMm5Kv9POoFAAAAAAAsdyuO8ZOUlKS1a9dq27Ztatq0qSTpzTffVFRUlPbt26caNWpkqWOM0cyZMzV+/Hj16NFDkvTOO+8oNDRU77//vp544gm5u7srLCzMod7KlSvVp08f+fv7O8wPCAjIUjY/yPgBAAAAAABwIj4+XkFBQfagjyQ1a9ZMQUFB2rp1q9M6Bw4cUEpKitq3b2+f5+3trVatWmVbJyEhQYmJiXrssceyLJs2bZqCg4PVoEEDvfDCC0pPT8/XPpDxAwAAAAAALHf5OjJ+0tLSlJaW5jDP29tb3t7e19WmlJQUlS5dOsv80qVLKyUlJds6khQaGuowPzQ0VL/++qvTOvPnz1etWrXUvHlzh/lPP/20GjVqpBIlSmjHjh2KiYnRgQMH9NZbb+V5H8j4AQAAAAAAlrueMX6mTp1qH3w5c5o6dWq224qNjc12cOXMadeuXZIkm82Wta3GOJ1/tWuXZ1fnwoULev/9951m+4wcOVKtWrVS/fr19fjjj2vu3LmaP3++Tpw4keO2r0bGDwAAAAAAsNz1jPETExOjUaNGOczLKdvnqaeeUt++fXNcZ8WKFbVnzx4dPXo0y7I//vgjS0ZPpszxeFJSUlSmTBn7/GPHjjmt8+GHH+r8+fN66KGHcmyPdOUxM0n66aefFBwcnGt5icAPAAAAAAC4BVzP27ny+1hXSEiIQkJCci0XFRWlM2fOaMeOHWrSpIkkafv27Tpz5kyWx7IyVapUSWFhYYqLi1PDhg0lSenp6dq0aZOmTZuWpfz8+fPVpUsXlSpVKtf27N69W5IcAkq5IfADAAAAAAAsdyu+1atWrVrq0KGDBg0apHnz5kmSBg8erM6dOzu80atmzZqaOnWqunfvLpvNphEjRujFF19UtWrVVK1aNb344ovy9fVVv379HNb/008/6csvv9SaNWuybDs+Pl7btm1TmzZtFBQUpJ07d2rkyJHq0qWLKlSokOd9IPADAAAAAACQjUWLFmn48OH2t3R16dJFs2bNciizb98+nTlzxv772LFjdeHCBT355JM6deqUmjZtqnXr1ikgIMCh3oIFC1S2bFmHN4Bl8vb21tKlSzVp0iSlpaUpIiJCgwYN0tixY/PVfpsxJk8hNaMN+VoxAAAAAADIP5vaWN0ES/x4Z+0C162+8/sb2JKihYwfAAAAAABguesZ4wfZI/ADAAAAAAAsd/kWHOOnKCDwAwAAAAAALHf5stUtKJoI/AAAAAAAAMsR+HENN6sbAAAAAAAAANcg4wcAAAAAAFiOjB/XIPADAAAAAAAsx9jOrkHgBwAAAAAAWI6MH9cg8AMAAAAAACxH4Mc1CPwAAAAAAADLEfhxDd7qBQAAAAAAUESR8QMAAAAAACxHxo9rEPgBAAAAAACWI/DjGgR+AAAAAACA5Qj8uAaBHwAAAAAAYDkCP65B4AcAAAAAAFiOwI9r8FYvAAAAAACAIoqMHwAAAAAAYDljjNVNKJII/AAAAAAAAMvxqJdrEPgBAAAAAACWI/DjGgR+AAAAAACA5Qj8uAaBHwAAAAAAYDkCP67BW70AAAAAAACKKDJ+AAAAAACA5cj4cQ0CPwAAAAAAwHIEflyDwA8AAAAAALAcgR/XIPADAAAAAAAsd9lY3YKiicAPAAAAAACwHBk/rsFbvQAAAAAAALJx6tQpDRgwQEFBQQoKCtKAAQN0+vTpHOusWLFC0dHRCgkJkc1mU2JiYpYyaWlpGjZsmEJCQuTn56cuXbro8OHD173taxH4AQAAAAAAlrt8ueCTK/Xr10+JiYlau3at1q5dq8TERA0YMCDHOn/++adatGihf/3rX9mWGTFihFauXKklS5Zoy5YtOnfunDp37qyMjIzr2va1bMaYPD1FZ7QhXysGAAAAAAD5Z1Mbq5tgiSXuNQpct2/GvhvYkv+TlJSk2rVra9u2bWratKkkadu2bYqKitIPP/ygGjVybvPBgwdVqVIl7d69Ww0aNLDPP3PmjEqVKqV3331Xffr0kSQdOXJE5cuX15o1axQdHX3d285Exg8AAAAAALDc9WT8pKWlKTU11WFKS0u77jbFx8crKCjIHniRpGbNmikoKEhbt24t8HoTEhJ08eJFtW/f3j4vPDxcdevWta/3Rm07z4M7F9WIY1pamqZOnaqYmBh5e3tb3RzkEf1WeNF3hRP9VjjRb4UT/VY40W+FF31XONFvRVM/U/CsndjYWE2aNMlh3sSJExUbG3tdbUpJSVHp0qWzzC9durRSUlKua71eXl4qUaKEw/zQ0FD7em/Utm/7jJ+0tDRNmjTphkQCcfPQb4UXfVc40W+FE/1WONFvhRP9VnjRd4UT/YZrxcTE6MyZMw5TTExMtuVjY2Nls9lynHbt2iVJstlsWeobY5zOv17XrvdGbJvXuQMAAAAAgELN29s7X9lfTz31lPr27ZtjmYoVK2rPnj06evRolmV//PGHQkND893OTGFhYUpPT9epU6ccsn6OHTum5s2b28vciG0T+AEAAAAAALeVkJAQhYSE5FouKipKZ86c0Y4dO9SkSRNJ0vbt23XmzBl7gKYgIiMj5enpqbi4OPXu3VuSlJycrO+++07Tp0+/odsm8AMAAAAAAOBErVq11KFDBw0aNEjz5s2TJA0ePFidO3d2eKtWzZo1NXXqVHXv3l2SdPLkSR06dEhHjhyRJO3bd2X8orCwMIWFhSkoKEiPPfaY/vGPfyg4OFglS5bU6NGjVa9ePbVt2zZf287NbT/Gj7e3tyZOnMiAYIUM/VZ40XeFE/1WONFvhRP9VjjRb4UXfVc40W+4mRYtWqR69eqpffv2at++verXr693333Xocy+fft05swZ+++rVq1Sw4YNdd9990mS+vbtq4YNG2ru3Ln2Mq+++qq6deum3r17q0WLFvL19dUnn3wid3f3fG07NzZjjCnIjgMAAAAAAODWdttn/AAAAAAAABRVBH4AAAAAAACKKAI/AAAAAAAARRSBHxerWLGiZs6caXUzblk2m00fffSR1c0ocg4ePCibzabExESXbmfjxo2y2Ww6ffq0S7dzO8ntM+GKa8rDDz+sbt265VgmP9tduHChihcvft3tgmtde53g84xM+f1ujo2NVYMGDVzWnlvNtfubl2vo7e52O0cA4Fbj8sDPww8/LJvNJpvNJk9PT1WuXFmjR4/Wn3/+6epN3xJ27typwYMHW90MS6SkpOjpp59W1apV5ePjo9DQUN11112aO3euzp8/b3XzCrWrP1c2m03BwcHq0KGD9uzZY2m7Mv9wzJxKlSqljh076ptvvrG0XbeSY8eO6YknnlCFChXk7e2tsLAwRUdHKz4+Pk/183NNiY2NdegPZ9PBgwdv+HZRcFb+Adm8eXMlJycrKCjIku3fDvJy7c5ctm3bNoe6aWlpCg4Ols1m08aNGx3K5zVIc+09WWhoqNq1a6cFCxbo8uXL9nLJycnq2LHjde1rfrnqHxYVK1a077Ovr6/q1q1rfx3ujfLvf/9bCxcuvKHrLKhrv4czpwkTJty0Njg7J0ePHq0vvvjiprUh09atW+Xu7q4OHTrc9G3fjn777Tc99thjCg8Pl5eXlyIiIvT000/rxIkTeV7HzfrnJXC7uSkZPx06dFBycrJ++eUXTZkyRbNnz9bo0aOzlLt48eLNaM5NVapUKfn6+lrdjJvul19+UcOGDbVu3Tq9+OKL2r17t9avX6+RI0fqk08+0fr1661uYqGX+blKTk7WF198IQ8PD3Xu3NnqZkm68irD5ORkrV69WqdOnVKHDh0cXm14tVvxc+/KNvXs2VPffPON3nnnHf34449atWqVWrdurZMnT+apfn6uKaNHj7afI8nJySpXrpwmT57sMK98+fI3fLsonLy8vBQWFiabzWZ1U4q0vFy7y5cvr7ffftth3sqVK+Xv73/Dtn/w4EF99tlnatOmjZ5++ml17txZly5dkiSFhYUVqdcjZ1739uzZo27dumnIkCFaunTpDVt/UFDQdWc5pqen35jG/H+Z38OZ0zPPPHND159f/v7+Cg4OvunbXbBggYYNG6YtW7bo0KFDN337t5NffvlFjRs31o8//qjFixfrp59+0ty5c/XFF18oKioqz/c5AFzjpgR+Mv+rXb58efXr10/9+/fXRx99ZE/7XLBggSpXrixvb28ZY3TmzBkNHjxYpUuXVmBgoO65554sGQNTpkxR6dKlFRAQoMcff1zPPPOM07TbGTNmqEyZMgoODtbQoUMd/qB777331LhxYwUEBCgsLEz9+vXTsWPH7Msz/2vyxRdfqHHjxvL19VXz5s21b98+h7asWrVKjRs3lo+Pj0JCQtSjRw/7smsfj8ht37755hu1adNGAQEBCgwMVGRkpHbt2nW9XXDTPfnkk/Lw8NCuXbvUu3dv1apVS/Xq1VPPnj21evVq/e1vf8tSx9ljBomJiVmyEr766iu1atVKvr6+KlGihKKjo3Xq1ClJV/4jOnz4cJUuXVo+Pj666667tHPnTnvdU6dOqX///ipVqpSKFSumatWqOdxc//777+rTp49KlCih4OBgde3aNc8ZETdb5ucqLCxMDRo00Lhx4/Tbb7/pjz/+cFp+06ZNatKkiby9vVWmTBk988wz9pt8KfdjJ0lr1qxR9erVVaxYMbVp0ybbY1O6dGmFhYWpSZMmevnll5WSkqJt27bZ/4vzwQcfqHXr1vLx8dF7770nSXr77bdVq1Yt+fj4qGbNmpo9e7Z9fenp6XrqqadUpkwZ+fj4qGLFipo6dap9eWxsrD2DJjw8XMOHD7cvc/afx+LFi9v/O1vQNhXE6dOntWXLFk2bNk1t2rRRRESEmjRpopiYGN13331O60yePFmhoaH2/3xde02x2Wx666231L17d/n6+qpatWpatWqVpCs32pnnSFhYmNzd3e3Xu6vnZcrpenntdk+fPq3BgwcrNDRUPj4+qlu3rj799FOn+3DixAk1adJEXbp00V9//ZXna+snn3yiyMhI+fj4qHLlypo0aZLDOZtTv8+ePVvVqlWzZxvef//9OXfOLah169YaPny4xo4dq5IlSyosLEyxsbEOZa7n3L/WtdfgzEf2Pv/8c9WqVUv+/v72oAEKLi/X7oEDB2rJkiW6cOGCfd6CBQs0cODAG7b9smXLqlGjRnr22Wf18ccf67PPPrOfG9eeO+PGjVP16tXl6+urypUr67nnnnMaIJ83b57Kly8vX19f9erVK8tjgzldUytVqiRJatiwoWw2m1q3bp2nerl9P0iyX/eqVq2qKVOmqFq1avb9y8s957/+9S+FhoYqICBAjz32mP766y+H5ddm6p09e1b9+/eXn5+fypQpo1dffVWtW7fWiBEj7GUqVqyoKVOm6OGHH1ZQUJAGDRok6UqGyt13361ixYqpfPnyGj58uEOWfHp6usaOHauyZcvKz89PTZs2dcgAy5T5PZw5+fv75+k+K6+f+wULFqhOnTr2e4qnnnrKvl+S1L17d9lsNvvv1z7qdfnyZU2ePFnlypWTt7e3GjRooLVr19qXZ343r1ixQm3atJGvr6/uuOOOPGfHStKff/6pDz74QH//+9/VuXPnLNe+VatWqVq1avZ7mnfeeSfL8cmtP/B/hg4dKi8vL61bt06tWrVShQoV1LFjR61fv16///67xo8fLyn376acrgXZnXeSdOjQIXXt2lX+/v4KDAxU7969dfToUfvyq//urFChgvz9/fX3v/9dGRkZmj59usLCwlS6dGm98MILDm3LyzUCKAwsGeOnWLFi9huGn376SR988IGWL19u/8PmvvvuU0pKitasWaOEhAQ1atRI9957rz1SvGjRIr3wwguaNm2aEhISVKFCBc2ZMyfLdjZs2KCff/5ZGzZs0DvvvKOFCxc6XPTT09P1z3/+U998840++ugjHThwQA8//HCW9YwfP14vv/yydu3aJQ8PDz366KP2ZatXr1aPHj103333affu3fY/ZJwxxuS6b/3791e5cuW0c+dOJSQk6JlnnpGnp2dBDrNlTpw4oXXr1mno0KHy8/NzWqag/1FOTEzUvffeqzp16ig+Pl5btmzR3/72N2VkZEiSxo4dq+XLl+udd97R119/rapVqyo6Otp+fJ977jl9//33+uyzz5SUlKQ5c+YoJCREknT+/Hm1adNG/v7++vLLL7Vlyxb7Dc+N/k/cjXbu3DktWrRIVatWdfoftd9//12dOnXSnXfeqW+++UZz5szR/PnzNWXKFHuZ3I7db7/9ph49eqhTp05KTEy0B1xzU6xYMUmOWTTjxo3T8OHDlZSUpOjoaL355psaP368XnjhBSUlJenFF1/Uc889p3feeUeS9Nprr2nVqlX64IMPtG/fPr333nv2m8kPP/xQr776qubNm6f9+/fro48+Ur169fJ9DPPbpoLw9/eXv7+/PvroI6WlpeVY1hijp59+WvPnz9eWLVtyHBth0qRJ6t27t/bs2aNOnTqpf//++f7PWm7Xy6tdvnxZHTt21NatW/Xee+/p+++/17/+9S+HIFKmw4cPq2XLlqpZs6ZWrFghHx8f+7Kcrq2ff/65HnzwQQ0fPlzff/+95s2bp4ULF9pvyHLq9127dmn48OGaPHmy9u3bp7Vr1+ruu+/O1/G4Vbzzzjvy8/PT9u3bNX36dE2ePFlxcXGSbty5n5Pz589rxowZevfdd/Xll1/q0KFDTjN2UTDZXbsjIyNVqVIlLV++XNKV6++XX36pAQMGuKQd99xzj+644w6tWLHC6fKAgAAtXLhQ33//vf7973/rzTff1KuvvupQJvN+7pNPPtHatWuVmJiooUOH2pfndk3dsWOHJGn9+vVKTk62t+V6vh+y4+Pjo4sXL+bpvuyDDz7QxIkT9cILL2jXrl0qU6ZMrv8EGDVqlL766iutWrVKcXFx2rx5s77++uss5V566SXVrVtXCQkJeu655/Ttt98qOjpaPXr00J49e7R06VJt2bLF4Y/bRx55RF999ZWWLFmiPXv2qFevXurQoYP279+fY5vyI7fP/Zw5czR06FANHjxY3377rVatWqWqVatKkv0fRm+//baSk5Oz/AMp07///W+9/PLLmjFjhvbs2aPo6Gh16dIly36MHz9eo0ePVmJioqpXr64HHnjA4R8AOVm6dKlq1KihGjVq6MEHH9Tbb78tY4ykK4Gl+++/X926dVNiYqKeeOIJe2AiU176A1ecPHlSn3/+uZ588kn7fV+msLAw9e/fX0uXLrUf/5xkdy3I6bwzxqhbt246efKkNm3apLi4OP3888/q06ePw7p//vlnffbZZ1q7dq0WL16sBQsW6L777tPhw4e1adMmTZs2TRMmTLA/apuXawRQaBgXGzhwoOnatav99+3bt5vg4GDTu3dvM3HiROPp6WmOHTtmX/7FF1+YwMBA89dffzmsp0qVKmbevHnGGGOaNm1qhg4d6rC8RYsW5o477nDYbkREhLl06ZJ9Xq9evUyfPn2ybeuOHTuMJHP27FljjDEbNmwwksz69evtZVavXm0kmQsXLhhjjImKijL9+/fPdp0RERHm1VdfzfO+BQQEmIULF2a7vsJg27ZtRpJZsWKFw/zg4GDj5+dn/Pz8zNixY40xxkgyK1euNMb83/E+deqUvc7u3buNJHPgwAFjjDEPPPCAadGihdPtnjt3znh6eppFixbZ56Wnp5vw8HAzffp0Y4wxf/vb38wjjzzitP78+fNNjRo1zOXLl+3z0tLSTLFixcznn3+er2PgagMHDjTu7u724ynJlClTxiQkJBhjjDlw4ICRZHbv3m2MMebZZ5/Nsm+vv/668ff3NxkZGXk6djExMaZWrVoO6xg3bpxDn13bh8ePHzddunQxAQEB5ujRo/Z2zZw502F/ypcvb95//32Hef/85z9NVFSUMcaYYcOGmXvuucdh25lefvllU716dZOenu70WF19jmUKCgoyb7/9tsOxym+bCurDDz80JUqUMD4+PqZ58+YmJibGfPPNNw7tXbZsmXnwwQdNzZo1zW+//eZQ/+prSmb5CRMm2H8/d+6csdls5rPPPsuy7WvrZsrL9fLqup9//rlxc3Mz+/btc7qPb7/9tgkKCjL79u0zFSpUMMOGDXPou7xcW1u2bGlefPFFh/W+++67pkyZMsaYnPt9+fLlJjAw0KSmpjpt363s6u/MVq1ambvuusth+Z133mnGjRtnjLlx537mdeLaz+/bb79tJJmffvrJXv/11183oaGh17eTt7Hcrt3G/F+/zZw507Rp08YYY8ykSZNM9+7dzalTp4wks2HDhizl87r9q+/JrtanTx9Tq1atPK1z+vTpJjIy0v77xIkTjbu7u8P16rPPPjNubm4mOTnZGJP7NfXa8zHT9Xw/GON47bp48aL9vJ49e3ae7suioqLMkCFDHJY3bdo0yz1n5nFNTU01np6eZtmyZfblp0+fNr6+vubpp592aFe3bt0c1jtgwAAzePBgh3mbN282bm5u5sKFC+ann34yNpvN/P777w5l7r33XhMTE2OM+b/PceY5ljkdP348T/dZefnch4eHm/Hjx5vsODt/Jk6c6HDMwsPDzQsvvOBQ5s477zRPPvmkMeb/zoe33nrLvnzv3r1GkklKSsp221dr3ry5/bv94sWLJiQkxMTFxRljrty/1K1b16H8+PHjHY5Pbv2B/5N575/ddeOVV14xkszRo0fz/d2UKafzbt26dcbd3d0cOnTIPi/zfNmxY4cx5so56Ovr63BvEB0dbSpWrGgyMjLs82rUqGGmTp1qjMnb325AYXFTMn4+/fRT+fv7y8fHR1FRUbr77rv1n//8R5IUERGhUqVK2csmJCTo3LlzCg4Otv933N/fXwcOHNDPP/8s6cpzy02aNHHYxrW/S1KdOnUc/gNdpkwZh0e5du/era5duyoiIkIBAQH2VMJrnwGuX7++wzok2deTmYGSF3nZt1GjRunxxx9X27Zt9a9//cs+vzC6Nqtnx44dSkxMVJ06dXLNdshOTsf7559/1sWLF9WiRQv7PE9PTzVp0kRJSUmSpL///e9asmSJGjRooLFjx2rr1q32sgkJCfrpp58UEBBg75uSJUvqr7/+uiX7oU2bNkpMTFRiYqK2b9+u9u3bq2PHjvr111+zlE1KSlJUVJRDn7Ro0ULnzp3T4cOH83TskpKS1KxZM4d1REVFOW1buXLl5O/vr5CQECUlJWnZsmUqXbq0ffnVWXF//PGHfTDAqz8XU6ZMsR/3hx9+WImJiapRo4aGDx+udevW2ev36tVLFy5cUOXKlTVo0CCtXLkyz/8NvFp+21RQPXv21JEjR7Rq1SpFR0dr48aNatSokUN2zciRIxUfH6/NmzerXLlyua7z6muUn5+fAgICHK51eZHb9fJqiYmJKleunKpXr57t+i5cuKC77rpL3bp102uvveY0yy+na2tCQoImT57scPwHDRqk5ORknT9/Psd+b9eunSIiIlS5cmUNGDBAixYtKrQDyl99jCTHfrlR535OfH19VaVKFafbR8Hk9dr94IMPKj4+Xr/88osWLlzokBHnCsaYbLNxP/zwQ9111132R4aee+65LPdKFSpUcLheRUVF6fLly9q3b1+Br6nX+/2Qady4cfL391exYsU0dOhQjRkzRk888USe7ssyvz+vlt13n3RlnJOLFy863JcGBQWpRo0aWcpemyGekJCghQsXOrQlOjpaly9f1oEDB/T111/LGKPq1as7lNm0aVOW47h582b7eZaYmKgSJUpk2+Zr5fS5P3bsmI4cOZLne19nUlNTdeTIEYd7DunKfUnmPUemnL4ncrJv3z7t2LFDffv2lSR5eHioT58+WrBggX35nXfe6VDn2r8lcusP5J35/5k+Bc34z+28S0pKUvny5R3GLaxdu7aKFy/ucE5VrFhRAQEB9t9DQ0NVu3Ztubm5Ocy7+l4kt2sEUFh43IyNtGnTRnPmzJGnp6fCw8MdHl269lGgy5cvq0yZMk6fV7564LxrLxyZF5SrXfuIlM1ms7+14s8//1T79u3Vvn17vffeeypVqpQOHTqk6OjoLI/1XL2ezO1mrufadMac5GXfYmNj1a9fP61evVqfffaZJk6cqCVLlqh79+553o7VqlatKpvNph9++MFhfuXKlSVlf8wyL7pX9+W1YwjkdLyz+1K5+mY28+Z69erVWr9+ve69914NHTpUM2bM0OXLlxUZGalFixZlWffVwclbhZ+fnz3FVbryaEBQUJDefPNNPf744w5lnd3QX3288nLsnH3GsrN582YFBgaqVKlSCgwMdNr2TJmfpTfffFNNmzZ1KJcZiGjUqJEOHDigzz77TOvXr1fv3r3Vtm1bffjhhypfvrz27dunuLg4rV+/Xk8++aReeuklbdq0SZ6eng77l8nZ2BT5bdP18PHxUbt27dSuXTs9//zzevzxxzVx4kT7o6bt2rXT4sWL9fnnn6t///65ri+na11e5WcdebnueXt7q23btlq9erXGjBnjNICV07X18uXLmjRpksOYaZl8fHxy7PeAgAB9/fXX2rhxo9atW6fnn39esbGx2rlzZ6F7zXxO/XKjzv38bj8/1wJkldO1++rHb4ODg9W5c2f7mDIdO3bU2bNnXdaupKQk+9gaV9u2bZv69u2rSZMmKTo6WkFBQVqyZIlefvnlHNeX+Zm++pzN7zX1er8fMo0ZM0YPP/ywfH19VaZMGYfrTV7uOfMjp+/Tazm7B37iiSccxurKVKFCBe3Zs0fu7u5KSEjIctyuHfi7UqVKWfYhL/dZUs6f+/zc9+Ymp3sOZ2259nsiJ/Pnz9elS5dUtmxZh/V7enrq1KlTOd4XZcqtP/B/Mu/9v//+e6dvpvzhhx9UokQJhYSEFOi7KbfzLrvA9bXznZ3bOX3PuuIaAVjlpmT8ZN7kRERE5DpeTaNGjZSSkiIPDw9VrVrVYcoci6VGjRr25z8z5XcA5B9++EHHjx/Xv/71L/v4EwX5L2b9+vXz/HrKvOybJFWvXl0jR47UunXr1KNHjyxv9rjVBQcHq127dpo1a1a+BsDLDK5cPYDgta9yzOl4V61aVV5eXtqyZYt93sWLF7Vr1y7VqlXLYTsPP/yw3nvvPc2cOVNvvPGGpCv9s3//fpUuXTpL/xSG1xvbbDa5ubk5DAaaqXbt2tq6davDF+3WrVsVEBCgsmXL5unY1a5dO8vrha/9PVOlSpVUpUoVp0Gfa4WGhqps2bL65Zdfshz3q/8ICQwMVJ8+ffTmm29q6dKlWr58uf356mLFiqlLly567bXXtHHjRsXHx+vbb7+VdKW/rz6n9u/fn2v2R17bdKPUrl3b4bPSpUsXvf/++3r88ce1ZMmSG76961W/fn0dPnxYP/74Y7Zl3Nzc9O677yoyMlL33HOPjhw5kq9tNGrUSPv27cty/KtWrWr/4yWnfvfw8FDbtm01ffp07dmzRwcPHtT//ve/gu/0LepGn/u4+XK6dj/66KPauHGjHnrooRsSdM7O//73P3377bfq2bNnlmVfffWVIiIiNH78eDVu3FjVqlVzmll66NAhh895fHy83NzcVL169TxdU728vCTJPmafdGO+HyQpJCREVatWVXh4uMMfgXm5L6tVq1aev/skqUqVKvL09HS4T01NTc3TGDyNGjXS3r17nV73vLy81LBhQ2VkZOjYsWNZloeFheW6/rzcZ+UmICBAFStWzPHe19PT06EfrxUYGKjw8HCHew7pyn3J1fdrBXXp0iX997//1csvv+yQ9fTNN98oIiJCixYtUs2aNbOMP3Tt3xK59Qf+T+a9/+zZs7Ncy1JSUrRo0SL16dNHNpst1+8mZ9eC3M672rVr69ChQ/rtt9/s877//nudOXPmus6pvP7tBhQGNyXjJz/atm2rqKgodevWTdOmTVONGjV05MgRrVmzRt26dVPjxo01bNgwDRo0SI0bN1bz5s21dOlS7dmzx55RkhcVKlSQl5eX/vOf/2jIkCH67rvv9M9//jPf7Z04caLuvfdeValSRX379tWlS5f02WefaezYsfnetzp16mjMmDG6//77ValSJR0+fFg7d+50eiN2q5s9e7ZatGihxo0bKzY2VvXr15ebm5t27typH374QZGRkVnqVK1aVeXLl1dsbKymTJmi/fv3Z/mPYkxMjOrVq6cnn3xSQ4YMkZeXlzZs2KBevXopJCREf//73zVmzBiVLFlSFSpU0PTp03X+/Hk99thjkqTnn39ekZGR9sfNPv30U/sXQv/+/fXSSy+pa9eu9jdNHDp0SCtWrMg2Y8FKaWlpSklJkXTlbWWzZs3SuXPnnL4x7cknn9TMmTM1bNgwPfXUU9q3b58mTpyoUaNGyc3NTX5+frkeuyFDhujll1/WqFGj7Cny2Q3+m1+xsbEaPny4AgMD1bFjR6WlpWnXrl06deqURo0apVdffVVlypRRgwYN5ObmpmXLliksLMz+FoiMjAw1bdpUvr6+evfdd1WsWDFFRERIujJo6axZs9SsWTNdvnxZ48aNy9OA6bm1qSBOnDihXr166dFHH1X9+vUVEBCgXbt2afr06eratatD2e7du+vdd9/VgAED5OHhcUu9lapVq1a6++671bNnT73yyiuqWrWqfvjhB9lsNnXo0MFezt3dXYsWLdIDDzyge+65Rxs3bszTHyfSlc9q586dVb58efXq1Utubm7as2ePvv32W02ZMiXHfv/000/1yy+/6O6771aJEiW0Zs0aXb582emjFoWZq859uFZ+rt0dOnTQH3/8kacgen63n5GRoaNHj2rt2rWaOnWqOnfurIceeihL+apVq+rQoUNasmSJ7rzzTq1evVorV67MUs7Hx0cDBw7UjBkzlJqaquHDh6t37972z3xu19TSpUurWLFiWrt2rcqVKycfHx8FBQVd1/dDbvJyz/n0009r4MCBaty4se666y4tWrRIe/fuzfaeMyAgQAMHDrR/n5YuXVoTJ06Um5tbro+5jBs3Ts2aNdPQoUM1aNAg+fn5KSkpSXFxcfrPf/6j6tWrq3///nrooYf08ssvq2HDhjp+/Lj+97//qV69eurUqVOO68/LfVZexMbGasiQISpdurQ9E+2rr77SsGHDJMn+B3qLFi3k7e3t9DGzMWPGaOLEiapSpYoaNGigt99+W4mJiU6zrvPr008/1alTp/TYY49l+cfd/fffr/nz52vFihV65ZVXNG7cOD322GNKTEx0eKudlHt/wNGsWbPUvHlzRUdHa8qUKapUqZL27t2rMWPGqGzZsvaXM+T23ZTTtSC7865t27aqX7+++vfvr5kzZ+rSpUt68skn1apVq2xfupMXeblGAIWGqwcRymkgwWsHesuUmppqhg0bZsLDw42np6cpX7686d+/v8OAXZMnTzYhISHG39/fPProo2b48OGmWbNmOW736aefNq1atbL//v7775uKFSsab29vExUVZVatWpXjQJfGZB0Ez5grA4k2aNDAeHl5mZCQENOjRw/7smsHU81p39LS0kzfvn1N+fLljZeXlwkPDzdPPfVUoR1A7siRI+app54ylSpVMp6ensbf3980adLEvPTSS+bPP/80xmQdAHDLli2mXr16xsfHx7Rs2dIsW7Ysy/HeuHGjad68ufH29jbFixc30dHR9j66cOGCGTZsmAkJCTHe3t6mRYsW9kHdjLkyIGStWrVMsWLFTMmSJU3Xrl3NL7/8Yl+enJxsHnroIXv9ypUrm0GDBpkzZ8649Fjl18CBA40k+xQQEGDuvPNO8+GHHxpjnA+Mt3HjRnPnnXcaLy8vExYWZsaNG2cuXrxoX57bsTPGmE8++cRUrVrVeHt7m5YtW5oFCxbkOLjztbIbsM8YYxYtWmT/HJUoUcLcfffd9gHC33jjDdOgQQPj5+dnAgMDzb333mu+/vprY4wxK1euNE2bNjWBgYHGz8/PNGvWzGHQ4N9//920b9/e+Pn5mWrVqpk1a9bkaRDB3NpUEH/99Zd55plnTKNGjUxQUJDx9fU1NWrUMBMmTDDnz583xmT9TCxdutT4+PiY5cuXG2OcD+6c0yCJV8tpcOfcrpfX1j1x4oR55JFHTHBwsPHx8TF169Y1n376qTHm/wZ3znTx4kXTo0cPU6tWLXP06NE8X1vXrl1rmjdvbooVK2YCAwNNkyZNzBtvvGGMybnfN2/ebFq1amVKlChhihUrZurXr2+WLl2aZb9vRdcO7nz1YLDGGNO1a1czcOBAY8yNP/edDe58dT9mbvMm3DoUWbldu43JeWDlGzG4c+a2PTw8TKlSpUzbtm3NggULHAY3vXadY8aMMcHBwcbf39/06dPHvPrqqw7nRub93OzZs014eLjx8fExPXr0MCdPnnTYfm7X1DfffNOUL1/euLm5OVx/Cvr9YEz2171MebnnfOGFF+z3nAMHDjRjx47NdnDnzHX269fP+Pr6mrCwMPPKK6+YJk2amGeeeSbXdu3YscO0a9fO+Pv7Gz8/P1O/fn2HQZDT09PN888/bypWrGg8PT1NWFiY6d69u9mzZ48xJvfv4dzus/L6uZ87d66pUaOG8fT0NGXKlDHDhg2zL1u1apWpWrWq8fDwMBEREcaYrPf8GRkZZtKkSaZs2bLG09PT3HHHHQ4vJXD23ezs/Hemc+fOplOnTk6XJSQkGEkmISHBfPzxx/Z7mtatW5s5c+Y4vGTAmNz7A44OHjxoHn74YRMWFmb/PA0bNswcP37cXia37yZjsr8W5HTe/frrr6ZLly7Gz8/PBAQEmF69epmUlBT7cmd/dzq7/7n2uzcv1wigMLAZUzQe1m/Xrp3CwsL07rvvWt0UAAAAQNKVcSXLli2rl19+2Z5Fi1vPCy+8oLlz5zo8LgQARcUt96hXXpw/f15z585VdHS03N3dtXjxYq1fv15xcXFWNw0AAAC3sd27d+uHH35QkyZNdObMGU2ePFmSsjzSC2vNnj1bd955p4KDg/XVV1/ppZde0lNPPWV1swDAJQpl4Mdms2nNmjWaMmWK0tLSVKNGDS1fvlxt27a1umkAAAC4zc2YMUP79u2Tl5eXIiMjtXnzZgaDvcXs379fU6ZM0cmTJ1WhQgX94x//UExMjNXNAgCXKDKPegEAAAAAAMDRTXmdOwAAAAAAAG4+Aj8AAAAAAABFFIEfAAAAAACAIorADwAAAAAAQBFF4AcAAAAAAKCIIvADAAAAAABQRBH4AQAAAAAAKKII/AAAAAAAABRRBH4AAAAAAACKqP8Hbfe3MUfAe8wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,4))\n",
    "sns.heatmap((dataset.isna().sum()).to_frame(name='').T,cmap='RdYlGn', annot=True,\n",
    "             fmt='0.0f').set_title('Count missing values ', fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rmAi_DuSbi5f",
   "metadata": {
    "id": "rmAi_DuSbi5f"
   },
   "source": [
    "### Checking Duplicate in each Rows and Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "42be46ba",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 18,
     "status": "ok",
     "timestamp": 1686345149549,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "42be46ba",
    "outputId": "58cb2cce-d438-4486-f004-04ae82d61ec1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of duplicates in each column: 0\n",
      "Number of duplicate rows: 0\n"
     ]
    }
   ],
   "source": [
    "duplicates_columns = dataset.duplicated()\n",
    "print(\"Number of duplicates in each column:\",duplicates_columns.sum())\n",
    "\n",
    "duplicates_rows = dataset.duplicated(keep=False)\n",
    "print(\"Number of duplicate rows:\",duplicates_rows.sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7KBjcPkJbp0k",
   "metadata": {
    "id": "7KBjcPkJbp0k"
   },
   "source": [
    "### Checking Unique values for each Attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1547d9dc",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 13,
     "status": "ok",
     "timestamp": 1686345149550,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "1547d9dc",
    "outputId": "bb0c803b-a12c-49be-fae7-fd910c187f7d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outcome : 2\n",
      "Pregnancies : 17\n",
      "BloodPressure : 47\n",
      "SkinThickness : 51\n",
      "Age : 52\n",
      "Glucose : 136\n",
      "Insulin : 186\n",
      "BMI : 248\n",
      "DiabetesPedigreeFunction : 517\n"
     ]
    }
   ],
   "source": [
    "for i in sorted(dataset, key=lambda x: len(dataset[x].unique())):\n",
    "    print(i, \":\", len(dataset[i].unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "gbORvC0gb4Dg",
   "metadata": {
    "id": "gbORvC0gb4Dg"
   },
   "source": [
    "### Data Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2c924d4f",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 332
    },
    "executionInfo": {
     "elapsed": 9,
     "status": "ok",
     "timestamp": 1686345149550,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "2c924d4f",
    "outputId": "9d3f5980-4c79-422e-95cd-2337747f3a93"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pregnancies</th>\n",
       "      <td>768.0</td>\n",
       "      <td>3.845052</td>\n",
       "      <td>3.369578</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>6.00000</td>\n",
       "      <td>17.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glucose</th>\n",
       "      <td>768.0</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>0.000</td>\n",
       "      <td>99.00000</td>\n",
       "      <td>117.0000</td>\n",
       "      <td>140.25000</td>\n",
       "      <td>199.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BloodPressure</th>\n",
       "      <td>768.0</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>0.000</td>\n",
       "      <td>62.00000</td>\n",
       "      <td>72.0000</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>122.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SkinThickness</th>\n",
       "      <td>768.0</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>23.0000</td>\n",
       "      <td>32.00000</td>\n",
       "      <td>99.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Insulin</th>\n",
       "      <td>768.0</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>30.5000</td>\n",
       "      <td>127.25000</td>\n",
       "      <td>846.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMI</th>\n",
       "      <td>768.0</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.000</td>\n",
       "      <td>27.30000</td>\n",
       "      <td>32.0000</td>\n",
       "      <td>36.60000</td>\n",
       "      <td>67.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <td>768.0</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>0.078</td>\n",
       "      <td>0.24375</td>\n",
       "      <td>0.3725</td>\n",
       "      <td>0.62625</td>\n",
       "      <td>2.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>768.0</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>21.000</td>\n",
       "      <td>24.00000</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>41.00000</td>\n",
       "      <td>81.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outcome</th>\n",
       "      <td>768.0</td>\n",
       "      <td>0.348958</td>\n",
       "      <td>0.476951</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          count        mean         std     min       25%  \\\n",
       "Pregnancies               768.0    3.845052    3.369578   0.000   1.00000   \n",
       "Glucose                   768.0  120.894531   31.972618   0.000  99.00000   \n",
       "BloodPressure             768.0   69.105469   19.355807   0.000  62.00000   \n",
       "SkinThickness             768.0   20.536458   15.952218   0.000   0.00000   \n",
       "Insulin                   768.0   79.799479  115.244002   0.000   0.00000   \n",
       "BMI                       768.0   31.992578    7.884160   0.000  27.30000   \n",
       "DiabetesPedigreeFunction  768.0    0.471876    0.331329   0.078   0.24375   \n",
       "Age                       768.0   33.240885   11.760232  21.000  24.00000   \n",
       "Outcome                   768.0    0.348958    0.476951   0.000   0.00000   \n",
       "\n",
       "                               50%        75%     max  \n",
       "Pregnancies                 3.0000    6.00000   17.00  \n",
       "Glucose                   117.0000  140.25000  199.00  \n",
       "BloodPressure              72.0000   80.00000  122.00  \n",
       "SkinThickness              23.0000   32.00000   99.00  \n",
       "Insulin                    30.5000  127.25000  846.00  \n",
       "BMI                        32.0000   36.60000   67.10  \n",
       "DiabetesPedigreeFunction    0.3725    0.62625    2.42  \n",
       "Age                        29.0000   41.00000   81.00  \n",
       "Outcome                     0.0000    1.00000    1.00  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe(include='all').T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5OIvI-pIcYSO",
   "metadata": {
    "id": "5OIvI-pIcYSO"
   },
   "source": [
    "# Plotting data to see how the distribution of Feature attributes with Target attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8bd65713",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 449
    },
    "executionInfo": {
     "elapsed": 480,
     "status": "ok",
     "timestamp": 1686345150023,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "8bd65713",
    "outputId": "84b29d0f-a174-4a5b-8baa-097fb86bdba1"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "dataset.plot(x='Outcome', y='Pregnancies', kind='scatter')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "969a9046",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 449
    },
    "executionInfo": {
     "elapsed": 9,
     "status": "ok",
     "timestamp": 1686345150024,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "969a9046",
    "outputId": "979ea2d9-36c9-430a-ba89-3f849b648a89"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset.plot(x='Outcome', y='Glucose', kind='scatter')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "98cfafdf",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 449
    },
    "executionInfo": {
     "elapsed": 660,
     "status": "ok",
     "timestamp": 1686345150676,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "98cfafdf",
    "outputId": "bf380602-9501-4b02-dcca-fd5359979bd3"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset.plot(x='Outcome', y='Insulin', kind='scatter')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5e9768d0",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 453
    },
    "executionInfo": {
     "elapsed": 9,
     "status": "ok",
     "timestamp": 1686345150677,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "5e9768d0",
    "outputId": "f3802d97-7838-46b7-dbaa-86ef3bfdcb7b"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset.plot(x='Outcome', y='BMI', kind='scatter')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8JTxEiTqd6TH",
   "metadata": {
    "id": "8JTxEiTqd6TH"
   },
   "source": [
    "### Checking Outliers by plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "65ae2af0",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 522
    },
    "executionInfo": {
     "elapsed": 855,
     "status": "ok",
     "timestamp": 1686345151525,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "65ae2af0",
    "outputId": "24767fdd-7088-4add-9288-0503b97e72c0"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset.plot(kind='box',figsize=(14,6))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "wjDcm3C9eB63",
   "metadata": {
    "id": "wjDcm3C9eB63"
   },
   "source": [
    "### Outlier counting "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0109b581",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 9,
     "status": "ok",
     "timestamp": 1686345151526,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "0109b581",
    "outputId": "76e6e587-5c11-44dc-d5e0-fab840c7781c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "╒══════════════════════════╤═══════════════╤═════════════════╤══════════════════════════════════════════════════════════════════════════╕\n",
      "│ Column                   │   Total Count │   Outlier Count │ Outlier Values                                                           │\n",
      "╞══════════════════════════╪═══════════════╪═════════════════╪══════════════════════════════════════════════════════════════════════════╡\n",
      "│ Pregnancies              │           768 │               4 │ [15 17 14 14]                                                            │\n",
      "├──────────────────────────┼───────────────┼─────────────────┼──────────────────────────────────────────────────────────────────────────┤\n",
      "│ Glucose                  │           768 │               5 │ [0 0 0 0 0]                                                              │\n",
      "├──────────────────────────┼───────────────┼─────────────────┼──────────────────────────────────────────────────────────────────────────┤\n",
      "│ BloodPressure            │           768 │              45 │ [  0   0  30 110   0   0   0   0 108 122  30   0 110   0   0   0   0   0 │\n",
      "│                          │               │                 │    0   0   0   0   0 108   0   0   0   0   0   0   0   0   0   0 110   0 │\n",
      "│                          │               │                 │   24   0   0   0   0 114   0   0   0]                                    │\n",
      "├──────────────────────────┼───────────────┼─────────────────┼──────────────────────────────────────────────────────────────────────────┤\n",
      "│ SkinThickness            │           768 │               1 │ [99]                                                                     │\n",
      "├──────────────────────────┼───────────────┼─────────────────┼──────────────────────────────────────────────────────────────────────────┤\n",
      "│ Insulin                  │           768 │              34 │ [543 846 342 495 325 485 495 478 744 370 680 402 375 545 360 325 465 325 │\n",
      "│                          │               │                 │  415 579 474 328 480 326 330 600 321 440 540 480 335 387 392 510]        │\n",
      "├──────────────────────────┼───────────────┼─────────────────┼──────────────────────────────────────────────────────────────────────────┤\n",
      "│ BMI                      │           768 │              19 │ [ 0.   0.   0.   0.  53.2 55.   0.  67.1 52.3 52.3 52.9  0.   0.  59.4   │\n",
      "│                          │               │                 │   0.   0.  57.3  0.   0. ]                                               │\n",
      "├──────────────────────────┼───────────────┼─────────────────┼──────────────────────────────────────────────────────────────────────────┤\n",
      "│ DiabetesPedigreeFunction │           768 │              29 │ [2.288 1.441 1.39  1.893 1.781 1.222 1.4   1.321 1.224 2.329 1.318 1.213 │\n",
      "│                          │               │                 │  1.353 1.224 1.391 1.476 2.137 1.731 1.268 1.6   2.42  1.251 1.699 1.258 │\n",
      "│                          │               │                 │  1.282 1.698 1.461 1.292 1.394]                                          │\n",
      "├──────────────────────────┼───────────────┼─────────────────┼──────────────────────────────────────────────────────────────────────────┤\n",
      "│ Age                      │           768 │               9 │ [69 67 72 81 67 67 70 68 69]                                             │\n",
      "├──────────────────────────┼───────────────┼─────────────────┼──────────────────────────────────────────────────────────────────────────┤\n",
      "│ Outcome                  │           768 │               0 │ []                                                                       │\n",
      "╘══════════════════════════╧═══════════════╧═════════════════╧══════════════════════════════════════════════════════════════════════════╛\n"
     ]
    }
   ],
   "source": [
    "from tabulate import tabulate\n",
    "\n",
    "# Create an empty list to store outlier information for each column\n",
    "outliers = []\n",
    "\n",
    "# Loop over each column in the DataFrame\n",
    "for col in dataset.columns:\n",
    "    # Calculate the first and third quartiles\n",
    "    q1 = dataset[col].quantile(0.25)\n",
    "    q3 = dataset[col].quantile(0.75)\n",
    "    \n",
    "    # Calculate the interquartile range (IQR)\n",
    "    iqr = q3 - q1\n",
    "    \n",
    "    # Calculate the lower and upper bounds for outliers\n",
    "    lower_bound = q1 - 1.5 * iqr\n",
    "    upper_bound = q3 + 1.5 * iqr\n",
    "    \n",
    "    # Find the outliers in the column\n",
    "    col_outliers = dataset[(dataset[col] < lower_bound) | (dataset[col] > upper_bound)][col]\n",
    "    \n",
    "    # Add the outlier information to the list\n",
    "    outliers.append({\n",
    "        'Column': col,\n",
    "        'Total Count': len(dataset[col]),\n",
    "        'Outlier Count': len(col_outliers),\n",
    "        'Outlier Values': col_outliers.values\n",
    "    })\n",
    "\n",
    "# Display the results in a table format\n",
    "print(tabulate(outliers, headers='keys', tablefmt='fancy_grid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d01c1947",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Assume that y is a list or array containing the class labels of your dataset\n",
    "unique_labels, counts = np.unique(dataset.Outcome, return_counts=True)\n",
    "\n",
    "plt.bar(unique_labels, counts)\n",
    "plt.xlabel('Class label')\n",
    "plt.ylabel('Number of samples')\n",
    "plt.title('Class distribution in dataset')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "vIbgfszKeepV",
   "metadata": {
    "id": "vIbgfszKeepV"
   },
   "source": [
    "### Correlation between the attributes those have large number of outlier and Target attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7d733f12",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 435
    },
    "executionInfo": {
     "elapsed": 826,
     "status": "ok",
     "timestamp": 1686345152347,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "7d733f12",
    "outputId": "1390390d-533c-4150-e0e8-45efcdcf7df7"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Select the columns of interest\n",
    "columns_of_interest = ['BloodPressure','Insulin','BMI','DiabetesPedigreeFunction', 'Outcome']\n",
    "\n",
    "# Calculate the correlation matrix between the selected columns\n",
    "corr = dataset[columns_of_interest].corr()['Outcome'].sort_values(ascending=False)\n",
    "\n",
    "# Display the correlation matrix for the selected columns as a heatmap\n",
    "sns.heatmap(corr.to_frame(), cmap='RdYlGn', annot=True, fmt='.2f');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "wENFHlL8YdOD",
   "metadata": {
    "id": "wENFHlL8YdOD"
   },
   "source": [
    "### Outlier Handling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "aS1ihqKhYkYm",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 15,
     "status": "ok",
     "timestamp": 1686345152348,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "aS1ihqKhYkYm",
    "outputId": "36fb789f-7b9b-4f34-e9ec-3de24d0a17b3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pregnancies True\n",
      "Glucose True\n",
      "BloodPressure True\n",
      "SkinThickness True\n",
      "Insulin True\n",
      "BMI True\n",
      "DiabetesPedigreeFunction True\n",
      "Age True\n",
      "Outcome False\n"
     ]
    }
   ],
   "source": [
    "# Outlier Analysis\n",
    "def outlier_thresholds(dataframe, col_name, q1=0.20, q3=0.80):\n",
    "    quartile1 = dataframe[col_name].quantile(q1)\n",
    "    quartile3 = dataframe[col_name].quantile(q3)\n",
    "    interquantile_range = quartile3 - quartile1\n",
    "    up_limit = quartile3 + 1.5 * interquantile_range\n",
    "    low_limit = quartile1 - 1.5 * interquantile_range\n",
    "    return low_limit, up_limit\n",
    "\n",
    "def check_outlier(dataframe, col_name):\n",
    "    low_limit, up_limit = outlier_thresholds(dataframe, col_name)\n",
    "    if dataframe[(dataframe[col_name] > up_limit) | (dataframe[col_name] < low_limit)].any(axis=None):\n",
    "        return True\n",
    "    else:\n",
    "        return False\n",
    "\n",
    "# We are checking the outlier analysis again for control\n",
    "for col in dataset.columns:\n",
    "    print(col, check_outlier(dataset, col))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "koZ0DxUkbC4B",
   "metadata": {
    "executionInfo": {
     "elapsed": 12,
     "status": "ok",
     "timestamp": 1686345152348,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "koZ0DxUkbC4B"
   },
   "outputs": [],
   "source": [
    "# We are replacing the outliers according to the IQR method.\n",
    "def replace_with_thresholds(dataframe, variable, q1=0.20, q3=0.80):\n",
    "    low_limit, up_limit = outlier_thresholds(dataframe, variable, q1=0.20, q3=0.80)\n",
    "    dataframe.loc[(dataframe[variable] < low_limit), variable] = low_limit\n",
    "    dataframe.loc[(dataframe[variable] > up_limit), variable] = up_limit\n",
    "    \n",
    "for col in dataset.columns:\n",
    "    replace_with_thresholds(dataset, col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "tXmxJRaAbChM",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 12,
     "status": "ok",
     "timestamp": 1686345152348,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "tXmxJRaAbChM",
    "outputId": "1e2389e8-9529-4ec4-f4e9-81819f73d7ea"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pregnancies False\n",
      "Glucose False\n",
      "BloodPressure False\n",
      "SkinThickness False\n",
      "Insulin False\n",
      "BMI False\n",
      "DiabetesPedigreeFunction False\n",
      "Age False\n",
      "Outcome False\n"
     ]
    }
   ],
   "source": [
    "# We are checking the outlier analysis again for control\n",
    "for col in dataset.columns:\n",
    "    print(col, check_outlier(dataset, col))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "HwdpgotE1ZtM",
   "metadata": {
    "id": "HwdpgotE1ZtM"
   },
   "source": [
    "# Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8XHN1ls60ZBN",
   "metadata": {
    "id": "8XHN1ls60ZBN"
   },
   "source": [
    "### Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8xcup9wI0cs8",
   "metadata": {
    "executionInfo": {
     "elapsed": 9,
     "status": "ok",
     "timestamp": 1686345152348,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "8xcup9wI0cs8"
   },
   "outputs": [],
   "source": [
    "#Replace Zeroes with mean values\n",
    "zero_not_accepted = ['Glucose','BloodPressure','SkinThickness', 'BMI','Insulin']\n",
    "    \n",
    "for column in zero_not_accepted:\n",
    "    dataset[column] = dataset[column].replace(0,np.NaN)\n",
    "    mean = int(dataset[column].mean(skipna=True))\n",
    "    dataset[column] = dataset[column].replace(np.NaN,mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4f6be762",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Assume that y is a list or array containing the class labels of your dataset\n",
    "unique_labels, counts = np.unique(dataset.columns, return_counts=True)  \n",
    "\n",
    "plt.bar(unique_labels, counts)\n",
    "plt.xlabel('Class label')\n",
    "plt.ylabel('Number of samples')\n",
    "plt.title('Class distribution in dataset')  #\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "OfKh_Zhq8E3W",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1686345152349,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "OfKh_Zhq8E3W",
    "outputId": "fa60eb69-2b8f-4c48-dad8-f37ed1e55dc3"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-014699ee-b8ac-4123-8ad3-f077850bce6c\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.6270</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.3510</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>29.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.6720</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.1670</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>43.1</td>\n",
       "      <td>1.3884</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>10</td>\n",
       "      <td>101</td>\n",
       "      <td>76</td>\n",
       "      <td>48.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.1710</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>2</td>\n",
       "      <td>122</td>\n",
       "      <td>70</td>\n",
       "      <td>27.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>36.8</td>\n",
       "      <td>0.3400</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>5</td>\n",
       "      <td>121</td>\n",
       "      <td>72</td>\n",
       "      <td>23.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.2450</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>1</td>\n",
       "      <td>126</td>\n",
       "      <td>60</td>\n",
       "      <td>29.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.3490</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>70</td>\n",
       "      <td>31.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.3150</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>768 rows × 9 columns</p>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-014699ee-b8ac-4123-8ad3-f077850bce6c')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-014699ee-b8ac-4123-8ad3-f077850bce6c button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-014699ee-b8ac-4123-8ad3-f077850bce6c');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0              6      148             72           35.0    147.0  33.6   \n",
       "1              1       85             66           29.0    147.0  26.6   \n",
       "2              8      183             64           29.0    147.0  23.3   \n",
       "3              1       89             66           23.0     94.0  28.1   \n",
       "4              0      137             40           35.0    168.0  43.1   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "763           10      101             76           48.0    180.0  32.9   \n",
       "764            2      122             70           27.0    147.0  36.8   \n",
       "765            5      121             72           23.0    112.0  26.2   \n",
       "766            1      126             60           29.0    147.0  30.1   \n",
       "767            1       93             70           31.0    147.0  30.4   \n",
       "\n",
       "     DiabetesPedigreeFunction   Age  Outcome  \n",
       "0                      0.6270  50.0      1.0  \n",
       "1                      0.3510  31.0      0.0  \n",
       "2                      0.6720  32.0      1.0  \n",
       "3                      0.1670  21.0      0.0  \n",
       "4                      1.3884  33.0      1.0  \n",
       "..                        ...   ...      ...  \n",
       "763                    0.1710  63.0      0.0  \n",
       "764                    0.3400  27.0      0.0  \n",
       "765                    0.2450  30.0      0.0  \n",
       "766                    0.3490  47.0      1.0  \n",
       "767                    0.3150  23.0      0.0  \n",
       "\n",
       "[768 rows x 9 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1Em5NRh1dcce",
   "metadata": {
    "id": "1Em5NRh1dcce"
   },
   "source": [
    "### Correlation with Target attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "83450452",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 583
    },
    "executionInfo": {
     "elapsed": 1077,
     "status": "ok",
     "timestamp": 1686345153417,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "83450452",
    "outputId": "ce9eba53-e133-4466-dfc0-d38a654d6ea7"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(dataset.drop('Outcome', axis=1)\n",
    "   .corrwith(dataset.Outcome)\n",
    "   .sort_values(ascending=False)\n",
    "   .plot(kind='bar', figsize=(12, 4), title=\"Correlation with Outcome\", alpha=0.8, zorder=3)\n",
    "   .spines[['top','right', 'left']].set_visible(False)\n",
    "   )\n",
    "plt.grid(axis='y', linestyle='--', alpha=.5)\n",
    "plt.xticks(rotation = 45, ha='right', size=15);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2dce738f",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 435
    },
    "executionInfo": {
     "elapsed": 1251,
     "status": "ok",
     "timestamp": 1686345154663,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "2dce738f",
    "outputId": "e6d8b858-6f74-46c3-9569-b4bd194b2e1d"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = dataset.corr()['Outcome'].sort_values(ascending=False)\n",
    "sns.heatmap(corr.to_frame(), cmap='RdYlGn', annot=True, fmt='.2f');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "HTMPWRILfQRF",
   "metadata": {
    "id": "HTMPWRILfQRF"
   },
   "source": [
    "### Using ExtraTreesClassifier to find the best feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "73836fd2",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 430
    },
    "executionInfo": {
     "elapsed": 2391,
     "status": "ok",
     "timestamp": 1686345157052,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "73836fd2",
    "outputId": "72986689-7ccb-4592-de66-c8903df7685b"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsIAAAGdCAYAAAAYIhVmAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA/Q0lEQVR4nO3deVyVdf7//+dB4LBDKAoaiAouNWLilmkuuYCpaVlamemI2phG5Ro1iloGkWaZ4zJqYmm5ZJmZS8oMqWhq5VYaHzeEErMpBXFBkPP7w5/n2xFQMOwA1+N+u1238VzX+3pfr+u8c86zd+9zHZPFYrEIAAAAMBgHexcAAAAA2ANBGAAAAIZEEAYAAIAhEYQBAABgSARhAAAAGBJBGAAAAIZEEAYAAIAhEYQBAABgSI72LgAozwoKCnTy5El5enrKZDLZuxwAAFACFotF586dU82aNeXgUPy8L0EYuIGTJ08qMDDQ3mUAAIBbkJGRoTvvvLPY4wRh4AY8PT0lXf2L5OXlZedqAABASWRnZyswMND6OV4cgjBwA9eWQ3h5eRGEAQCoYG62rJEvywEAAMCQCMIAAAAwJIIwAAAADIkgDAAAAEMiCAMAAMCQCMIAAAAwJIIwAAAADIkgDAAAAEPiBzWAEvhb7EY5mN3sXQZQSFp8d3uXAAAVFjPCAAAAMCSCMAAAAAyJIAwAAABDIggDAADAkAjCAAAAMCSCMAAAAAyJIFyOmUwmrV69utjjwcHBevvtt8v0mh06dNALL7zwp+r6o0mTJumee+7503UBAACUNYKwHf36668aPny4goKCZDab5e/vr4iICKWkpJTo/N27d2vYsGElajtp0iSZTKYbbiWVmZmpbt26lbg9AABAecQPathRnz59dPnyZS1evFh169bVL7/8oqSkJP32228lOt/Pz6/E1xozZoz+8Y9/WF+3aNFCw4YN09ChQ0tdt7+/f6nPAQAAKG+YEbaTs2fPauvWrXrjjTfUsWNH1a5dWy1btlRMTIweeuihIs+JjY1VQECA9u/fL6nw0giTyaQFCxbo4Ycflpubm0JDQ7VmzRpJkoeHh/z9/a1blSpV5OnpabPvmoKCAo0bN06+vr7y9/fXpEmTbOq4fmnETz/9pCeeeEK+vr5yd3dX8+bNtXPnziLv4ejRo6pbt65Gjhwpi8WixMRE+fj4aOPGjWrUqJE8PDwUGRmpzMxMm/MWLFigRo0aycXFRQ0bNtTs2bOtxy5fvqyRI0cqICBALi4uql27tuLi4iRJFotFkyZNss6616xZU9HR0TceHAAAYAgEYTvx8PCQh4eHVq9erdzc3Bu2tVgseu655/T+++9r69atCgsLK7bt5MmT1bdvX+3fv18PPvig+vfvr99//71UtS1evFju7u7auXOnEhISNGXKFG3atKnItjk5OWrfvr1+/vlnrVmzRvv27dO4ceNUUFBQqO3+/fvVtm1bPfnkk5o1a5Z1OcaFCxc0bdo0ffDBB9qyZYvS09M1ZswY63lLly7VxIkTNXXqVB06dEivv/66JkyYoMWLF0uSZs6cqTVr1mjFihVKTU3V0qVLFRwcLElatWqVZsyYoXnz5unw4cNavXq1GjduXOy95+bmKjs722YDAACVE0sj7MTR0VGJiYkaOnSo5s6dq/DwcLVv316PP/64TdDNz8/XU089pT179mjbtm2qVavWDfsdNGiQnnjiCUnS66+/rpkzZ2rXrl2KjIwscW1hYWGKjY2VJIWGhmrWrFlKSkpSly5dCrX98MMP9euvv2r37t3y9fWVJIWEhBRqt337dvXo0UOvvPKKRo8ebXMsLy9Pc+fOVb169SRJI0eO1JQpU6zHY2NjNX36dD3yyCOSpDp16ujgwYOaN2+eBg4cqPT0dIWGhqpt27YymUyqXbu29dz09HT5+/urc+fOcnJyUlBQkFq2bFnsvcfFxWny5MklfasAAEAFxoywHfXp00cnT57UmjVrFBkZqeTkZIWHhysxMdHa5sUXX9TOnTu1ZcuWm4ZgSTYh2t3dXV5eXjp9+nSp6rp+xjkgIKDYPvbu3aumTZtaQ3BR0tPT1aVLF02cOLFQCJYkNzc3awi+/nrnz5/X0aNHFRUVZZ1F9/Dw0GuvvaajR49Kuhr+9+7dqwYNGig6Olpffvmlta/HHntMFy9eVN26dTV06FB9+umnys/PL7bWmJgYZWVlWbeMjIxi2wIAgIqNIGxnLi4u6tKliyZMmKDt27dr0KBB1tlYSerSpYt+/vlnbdy4sUT9OTk52bw2mUxFLlMoqz5cXV1v2p+fn59atmypjz76qMilBkVdz2KxSLq69EKS5s+fr71791q377//Xl9//bUkKTw8XMePH9err76qixcvqm/fvnr00UclSYGBgUpNTdXs2bPl6uqqZ599Vu3atVNeXl6RtZrNZnl5edlsAACgciIIlzN33XWXzp8/b3390EMP6cMPP9SQIUO0bNkyO1ZWtLCwMO3du/eG65BdXV21du1aubi4KCIiQufOnStx/zVq1FDNmjV17NgxhYSE2Gx16tSxtvPy8lK/fv00f/58LV++XKtWrbLW5Orqqp49e2rmzJlKTk7Wjh07dODAgVu/aQAAUCmwRthOfvvtNz322GMaPHiwwsLC5OnpqW+++UYJCQnq1auXTduHH35YH3zwgQYMGCBHR0frbGd58MQTT+j1119X7969FRcXp4CAAO3Zs0c1a9ZU69atre3c3d31xRdfqFu3burWrZs2bNggDw+PEl1j8uTJio6Olre3tyIjI5Wbm6tvvvlGZ86c0ahRo/TWW28pICBATZs2lYODg1auXCl/f3/5+PgoMTFRV65cUatWreTm5qYlS5bI1dXVZh0xAAAwJoKwnXh4eKhVq1aaMWOGjh49qry8PAUGBmro0KF6+eWXC7V/9NFHVVBQoAEDBsjBwcH6xTF7c3Z21pdffqnRo0frwQcfVH5+vu666y7961//KtTWw8ND69evV0REhLp3765169aV6BpDhgyRm5ub3nzzTY0dO1bu7u5q3Lix9RfwPD09lZCQoMOHD6tKlSpq0aKF1q1bJwcHB/n4+Cg+Pl6jRo3SlStX1LhxY33++eeqWrVqWb4NAACgAjJZri3GBFBIdna2vL29FfjCCjmY3exdDlBIWnx3e5cAAOXOtc/vrKysG37fhzXCAAAAMCSCMAAAAAyJIAwAAABDIggDAADAkHhqBFAC30+O4Mc1AACoZJgRBgAAgCERhAEAAGBIBGEAAAAYEkEYAAAAhkQQBgAAgCERhAEAAGBIBGEAAAAYEkEYAAAAhkQQBgAAgCERhAEAAGBIBGEAAAAYEkEYAAAAhkQQBgAAgCERhAEAAGBIBGEAAAAYEkEYAAAAhkQQBgAAgCERhAEAAGBIBGEAAAAYEkEYAAAAhkQQBgAAgCERhAEAAGBIjvYuAKgI/ha7UQ5mN3uXAdx2afHd7V0CAPxlmBEGAACAIRGEAQAAYEgEYQAAABgSQRgAAACGRBAGAACAIRGEAQAAYEgEYVRYaWlpMplM2rt3ryQpOTlZJpNJZ8+etWtdAACgYiAIo0wNGjRIvXv3tsu177vvPmVmZsrb29su1wcAABULP6iBSsPZ2Vn+/v72LgMAAFQQzAjjtunQoYOio6M1btw4+fr6yt/fX5MmTbIet1gsmjRpkoKCgmQ2m1WzZk1FR0dbj5tMJq1evdqmTx8fHyUmJhZ5veuXRiQmJsrHx0cbN25Uo0aN5OHhocjISGVmZpbxnQIAgIqIIIzbavHixXJ3d9fOnTuVkJCgKVOmaNOmTZKkVatWacaMGZo3b54OHz6s1atXq3HjxmV6/QsXLmjatGn64IMPtGXLFqWnp2vMmDHFts/NzVV2drbNBgAAKieWRuC2CgsLU2xsrCQpNDRUs2bNUlJSkrp06aL09HT5+/urc+fOcnJyUlBQkFq2bFmm18/Ly9PcuXNVr149SdLIkSM1ZcqUYtvHxcVp8uTJZVoDAAAon5gRxm0VFhZm8zogIECnT5+WJD322GO6ePGi6tatq6FDh+rTTz9Vfn5+mV7fzc3NGoKvv35RYmJilJWVZd0yMjLKtB4AAFB+EIRxWzk5Odm8NplMKigokCQFBgYqNTVVs2fPlqurq5599lm1a9dOeXl51rYWi8Xm/GvH/sz1r+/zj8xms7y8vGw2AABQORGEYVeurq7q2bOnZs6cqeTkZO3YsUMHDhyQJPn5+dl8se3w4cO6cOGCvUoFAACVDGuEYTeJiYm6cuWKWrVqJTc3Ny1ZskSurq6qXbu2JOmBBx7QrFmz1Lp1a125ckXjx48vNMMLAABwq5gRht34+Pho/vz5atOmjcLCwrR582Z9/vnnqlq1qiRp+vTpCgwM1P33368nn3xSY8aMkZubm52rBgAAlYXJcqMFk4DBZWdny9vbW4EvrJCDmRCOyi8tvru9SwCAP+3a53dWVtYNv+/DjDAAAAAMiSAMAAAAQyIIAwAAwJAIwgAAADAkgjAAAAAMiecIAyXw/eQIfmUOAIBKhhlhAAAAGBJBGAAAAIZEEAYAAIAhEYQBAABgSARhAAAAGBJBGAAAAIZEEAYAAIAhEYQBAABgSARhAAAAGBJBGAAAAIZEEAYAAIAhEYQBAABgSARhAAAAGBJBGAAAAIZEEAYAAIAhEYQBAABgSARhAAAAGBJBGAAAAIZEEAYAAIAhEYQBAABgSARhAAAAGBJBGAAAAIbkaO8CgIrgb7Eb5WB2s3cZwF8qLb67vUsAgNuKGWEAAAAYEkEYAAAAhkQQBgAAgCERhAEAAGBIBGEAAAAYEkG4nEpLS5PJZNLevXtv63WSk5NlMpl09uzZ23odAACA8oYgbCeDBg2SyWSyblWrVlVkZKT2799v17quBeNrW40aNdSnTx8dO3bMrnUBAACUNYKwHUVGRiozM1OZmZlKSkqSo6OjevToYe+yJEmpqak6efKkVq5cqR9++EE9e/bUlStXCrWzWCzKz8+3Q4XFK481AQCA8ocgbEdms1n+/v7y9/fXPffco5deekkZGRn69ddfi2z/1VdfqWXLljKbzQoICNBLL71kE/hyc3MVHR2t6tWry8XFRW3bttXu3btt+li3bp3q168vV1dXdezYUWlpaUVeq3r16goICFC7du00ceJEHTx4UEeOHLHOGK9fv17NmjWT2WzWtm3bVFBQoLi4ONWpU0eurq5q0qSJPv74Y2t/Z86cUf/+/eXn5ydXV1eFhoZq0aJFkqTLly9r5MiRCggIkIuLi2rXrq24uDhJRS8ROXv2rEwmk5KTkyXplmsCAADGxi/LlRM5OTlasmSJQkJCVLVqVZ0/f97m+M8//6wHH3xQgwYN0vvvv68ff/xRQ4cOlYuLiyZNmiRJGjdunFatWqXFixerdu3aSkhIUEREhI4cOSJfX19lZGTokUce0YgRIzRs2DB98803Gj169E1rc3V1lXQ1sF7z0ksvadq0aapbt67uuOMOxcXFacmSJZo7d65CQ0O1ZcsWPfXUU/Lz81P79u01YcIEHTx4UOvXr1e1atV05MgRXbx4UZI0c+ZMrVmzRitWrFBQUJAyMjKUkZFR6vewtDUVJTc3V7m5udbX2dnZpa4DAABUDARhO1q7dq08PDwkSefPn1dAQIDWrl0rB4fCE/WzZ89WYGCgZs2aJZPJpIYNG+rkyZMaP368Jk6cqIsXL2rOnDlKTExUt27dJEnz58/Xpk2btHDhQo0dO1Zz5sxRvXr1NH36dElSgwYNdODAAb3xxhvF1piZmalp06apVq1aatCggbZv3y5JmjJlirp06SLpanh8/fXXtXnzZrVu3VqSVLduXW3btk3z5s1T+/btlZ6erqZNm6p58+aSpODgYOs10tPTFRoaqrZt28pkMql27dq39H6WtqaixMXFafLkybd0fQAAULEQhO2oY8eOmjNnjqSrSwdmz56tbt26adeuXYXaHjp0SK1bt5bJZLLua9OmjXJycvTTTz/p7NmzysvLU5s2bazHnZyc1LJlSx06dMjaR6tWrWz6vRYSr3fnnXfKYrHowoULatKkiVatWiVnZ2fr8WuBVpKOHDmiCxcuWEPoNZcvX1bTpk0lScOHD1efPn303XffqWvXrurdu7fuu+8+SVe/ONilSxc1aNBAkZGR6tGjh7p27XrzN/A6pa2pKDExMRo1apT1dXZ2tgIDA0tdCwAAKP8Iwnbk7u6ukJAQ6+sFCxbI29tb8+fP15AhQ+xYmbR161Z5eXmpevXq8vT0LHTc3d3d+uecnBxJ0hdffKFatWrZtDObzZKkbt266cSJE1q3bp02bdqkTp06acSIEZo2bZrCw8N1/PhxrV+/Xps3b1bfvn3VuXNnffzxx9bZcYvFYu0zLy+vyJpLW1NRzGbzDY8DAIDKgyBcjphMJjk4OFjXzv5Ro0aNtGrVKlksFuuscEpKijw9PXXnnXeqatWqcnZ2VkpKinVpQV5ennbv3q0XXnjB2seaNWts+v3666+LrKVOnTry8fEpUd133XWXzGaz0tPTi11yIEl+fn4aOHCgBg4cqPvvv19jx47VtGnTJEleXl7q16+f+vXrp0cffVSRkZH6/fff5efnJ+nqEo1rM7klebZySWsCAADGRRC2o9zcXJ06dUrS1aURs2bNUk5Ojnr27Fmo7bPPPqu3335bzz33nEaOHKnU1FTFxsZq1KhRcnBwkLu7u4YPH66xY8fK19dXQUFBSkhI0IULFxQVFSVJ+sc//qHp06dr7NixGjJkiL799lslJib+6fvw9PTUmDFj9OKLL6qgoEBt27ZVVlaWUlJS5OXlpYEDB2rixIlq1qyZ7r77buXm5mrt2rVq1KiRJOmtt95SQECAmjZtKgcHB61cuVL+/v7y8fGRg4OD7r33XsXHx6tOnTo6ffq0/vnPf5ZJTQAAwNgIwna0YcMGBQQESLoa3Bo2bKiVK1eqQ4cOhR5rVqtWLa1bt05jx45VkyZN5Ovrq6ioKJtQGB8fr4KCAg0YMEDnzp1T8+bNtXHjRt1xxx2SpKCgIK1atUovvvii3n33XbVs2VKvv/66Bg8e/Kfv5dVXX5Wfn5/i4uJ07Ngx+fj4KDw8XC+//LIkydnZWTExMUpLS5Orq6vuv/9+LVu2zHrvCQkJOnz4sKpUqaIWLVpo3bp11mUR7733nqKiotSsWTM1aNBACQkJJVpDfLOaAACAsZksf1x8CcBGdna2vL29FfjCCjmY3exdDvCXSovvbu8SAOCWXPv8zsrKkpeXV7Ht+EENAAAAGBJBGAAAAIZEEAYAAIAhEYQBAABgSARhAAAAGBKPTwNK4PvJETf81ikAAKh4mBEGAACAIRGEAQAAYEgEYQAAABgSQRgAAACGRBAGAACAIRGEAQAAYEgEYQAAABgSQRgAAACGRBAGAACAIRGEAQAAYEgEYQAAABgSQRgAAACGRBAGAACAIRGEAQAAYEgEYQAAABgSQRgAAACGRBAGAACAIRGEAQAAYEgEYQAAABgSQRgAAACGRBAGAACAIRGEAQAAYEiO9i4AqAj+FrtRDmY3e5cBVEhp8d3tXQIAFIkZYQAAABgSQRgAAACGRBAGAACAIRGEAQAAYEgEYQAAABgSQRgVislk0urVq+1dBgAAqAQIwhXMoEGDZDKZZDKZ5OzsrJCQEE2ZMkX5+fn2Lu0vkZmZqW7dutm7DAAAUAnwHOEKKDIyUosWLVJubq7WrVunESNGyMnJSTExMTbtLl++LGdnZztVeXv4+/vbuwQAAFBJMCNcAZnNZvn7+6t27doaPny4OnfurDVr1mjQoEHq3bu3pk6dqpo1a6pBgwaSpIyMDPXt21c+Pj7y9fVVr169lJaWZu0vPz9f0dHR8vHxUdWqVTV+/HgNHDhQvXv3trbp0KGDoqOjNW7cOPn6+srf31+TJk2yqeutt95S48aN5e7ursDAQD377LPKycmxHk9MTJSPj482btyoRo0aycPDQ5GRkcrMzLTp57333tPdd98ts9msgIAAjRw50nrs+qURN7u35ORktWzZUu7u7vLx8VGbNm104sSJW3/zAQBApUEQrgRcXV11+fJlSVJSUpJSU1O1adMmrV27Vnl5eYqIiJCnp6e2bt2qlJQUawC9ds4bb7yhpUuXatGiRUpJSVF2dnaR63AXL14sd3d37dy5UwkJCZoyZYo2bdpkPe7g4KCZM2fqhx9+0OLFi/Wf//xH48aNs+njwoULmjZtmj744ANt2bJF6enpGjNmjPX4nDlzNGLECA0bNkwHDhzQmjVrFBISUuR93+ze8vPz1bt3b7Vv31779+/Xjh07NGzYMJlMpmLfy9zcXGVnZ9tsAACgcmJpRAVmsViUlJSkjRs36rnnntOvv/4qd3d3LViwwLokYsmSJSooKNCCBQusAXDRokXy8fFRcnKyunbtqnfffVcxMTF6+OGHJUmzZs3SunXrCl0vLCxMsbGxkqTQ0FDNmjVLSUlJ6tKliyTphRdesLYNDg7Wa6+9pn/84x+aPXu2dX9eXp7mzp2revXqSZJGjhypKVOmWI+/9tprGj16tJ5//nnrvhYtWhR5/8uXL7/hvTVv3lxZWVnq0aOH9XqNGjW64XsaFxenyZMn37ANAACoHJgRroDWrl0rDw8Pubi4qFu3burXr591mULjxo1t1gXv27dPR44ckaenpzw8POTh4SFfX19dunRJR48eVVZWln755Re1bNnSek6VKlXUrFmzQtcNCwuzeR0QEKDTp09bX2/evFmdOnVSrVq15OnpqQEDBui3337ThQsXrG3c3NysofT6Pk6fPq2TJ0+qU6dOJXofbnZvvr6+GjRokCIiItSzZ0+98847hZZhXC8mJkZZWVnWLSMjo0S1AACAiocZ4QqoY8eOmjNnjpydnVWzZk05Ov6/YXR3d7dpm5OTo2bNmmnp0qWF+vHz8yvVdZ2cnGxem0wmFRQUSJLS0tLUo0cPDR8+XFOnTpWvr6+2bdumqKgoXb58WW5ubsX2YbFYJF1d4lEaJbm3RYsWKTo6Whs2bNDy5cv1z3/+U5s2bdK9995bZJ9ms1lms7lUdQAAgIqJIFwBubu7F7tu9nrh4eFavny5qlevLi8vryLb1KhRQ7t371a7du0kSVeuXNF3332ne+65p8Q1ffvttyooKND06dPl4HD1PzSsWLGixOdLkqenp4KDg5WUlKSOHTvetH1J7k2SmjZtqqZNmyomJkatW7fWhx9+WGwQBgAAxsHSiEquf//+qlatmnr16qWtW7fq+PHjSk5OVnR0tH766SdJ0nPPPae4uDh99tlnSk1N1fPPP68zZ87c8Etl1wsJCVFeXp7effddHTt2TB988IHmzp1b6nonTZqk6dOna+bMmTp8+LC+++47vfvuu7d0b8ePH1dMTIx27NihEydO6Msvv9Thw4dvuk4YAAAYA0G4knNzc9OWLVsUFBSkRx55RI0aNVJUVJQuXbpknUUdP368nnjiCT399NNq3bq1PDw8FBERIRcXlxJfp0mTJnrrrbf0xhtv6G9/+5uWLl2quLi4Utc7cOBAvf3225o9e7buvvtu9ejRQ4cPH76le3Nzc9OPP/6oPn36qH79+ho2bJhGjBihZ555ptR1AQCAysdkubZAE/j/FRQUqFGjRurbt69effVVe5djV9nZ2fL29lbgCyvkYHazdzlAhZQW393eJQAwmGuf31lZWTdcPskaYViXDbRv3165ubmaNWuWjh8/rieffNLepQEAANw2LI2AHBwclJiYqBYtWqhNmzY6cOCANm/ezFpaAABQqTEjDAUGBiolJcXeZQAAAPylmBEGAACAIRGEAQAAYEgsjQBK4PvJETf81ikAAKh4mBEGAACAIRGEAQAAYEgEYQAAABgSQRgAAACGRBAGAACAIRGEAQAAYEgEYQAAABgSQRgAAACGRBAGAACAIRGEAQAAYEgEYQAAABgSQRgAAACGRBAGAACAIRGEAQAAYEgEYQAAABgSQRgAAACGRBAGAACAIRGEAQAAYEgEYQAAABgSQRgAAACGRBAGAACAIRGEAQAAYEiO9i4AqAj+FrtRDmY3e5cB4A/S4rvbuwQAFRwzwgAAADAkgjAAAAAMiSAMAAAAQyIIAwAAwJAIwgAAADCk2xKETSaTVq9eXeL2kyZN0j333HM7SimXBg0apN69e1tfd+jQQS+88ILd6qkIrn/PAAAA/qxSBeFBgwbJZDLJZDLJyclJNWrUUJcuXfTee++poKDA2i4zM1PdunUr82JvJC0tTSaTSXv37i3TfoODg6337O7urvDwcK1cubJMr/HJJ5/o1VdfLdM+b1ViYqL1fv+4LViw4C+5fnHj+M477ygxMfEvqQEAABhDqWeEIyMjlZmZqbS0NK1fv14dO3bU888/rx49eig/P1+S5O/vL7PZXObF2suUKVOUmZmpPXv2qEWLFurXr5+2b99eZv37+vrK09PzT/WRl5dXRtVIXl5eyszMtNn69+9fZv3fCm9vb/n4+Ni1BgAAULmUOgibzWb5+/urVq1aCg8P18svv6zPPvtM69evt87YXb80Yvz48apfv77c3NxUt25dTZgwocjgNm/ePAUGBsrNzU19+/ZVVlaWzfEFCxaoUaNGcnFxUcOGDTV79mzrsTp16kiSmjZtKpPJpA4dOpTovMuXL2vkyJEKCAiQi4uLateurbi4OJvrenp6yt/fX/Xr19e//vUvubq66vPPP5ckZWRkqG/fvvLx8ZGvr6969eqltLQ067lXrlzRqFGj5OPjo6pVq2rcuHGyWCw2/V+/NCIzM1Pdu3eXq6ur6tSpow8//FDBwcF6++23rW1MJpPmzJmjhx56SO7u7po6daok6bPPPlN4eLhcXFxUt25dTZ482fovKJJ09uxZDRkyRH5+fvLy8tIDDzygffv22dRjMpnk7+9vs7m6uioxMbFQGF29erVMJpP19bVlLh988IGCg4Pl7e2txx9/XOfOnbO2KSgoUEJCgkJCQmQ2mxUUFGStv7hxvH5pRG5urqKjo1W9enW5uLiobdu22r17t/V4cnKyTCaTkpKS1Lx5c7m5uem+++5TamqqAAAApDJaI/zAAw+oSZMm+uSTT4o87unpqcTERB08eFDvvPOO5s+frxkzZti0OXLkiFasWKHPP/9cGzZs0J49e/Tss89ajy9dulQTJ07U1KlTdejQIb3++uuaMGGCFi9eLEnatWuXJGnz5s3KzMy01nKz82bOnKk1a9ZoxYoVSk1N1dKlSxUcHFzsvTo6OsrJyUmXL19WXl6eIiIi5Onpqa1btyolJUUeHh6KjIzU5cuXJUnTp09XYmKi3nvvPW3btk2///67Pv300xu+n08//bROnjyp5ORkrVq1Sv/+9791+vTpQu0mTZqkhx9+WAcOHNDgwYO1detWPf3003r++ed18OBBzZs3T4mJidaQKUmPPfaYTp8+rfXr1+vbb79VeHi4OnXqpN9///2GNZXG0aNHtXr1aq1du1Zr167VV199pfj4eOvxmJgYxcfHa8KECTp48KA+/PBD1ahRQ1Lx43i9cePGadWqVVq8eLG+++47hYSEKCIiotB9vPLKK5o+fbq++eYbOTo6avDgwWV2nwAAoGIrs59Ybtiwofbv31/ksX/+85/WPwcHB2vMmDFatmyZxo0bZ91/6dIlvf/++6pVq5Yk6d1331X37t01ffp0+fv7KzY2VtOnT9cjjzwi6erM4bWwN3DgQPn5+UmSqlatKn9/f2u/NzsvPT1doaGhatu2rUwmk2rXrl3sPV6+fFnTp09XVlaWHnjgAS1fvlwFBQVasGCBdVZ00aJF8vHxUXJysrp27aq3335bMTEx1uvPnTtXGzduLPYaP/74ozZv3qzdu3erefPmkq7OaIeGhhZq++STT+rvf/+79fXgwYP10ksvaeDAgZKkunXr6tVXX9W4ceMUGxurbdu2adeuXTp9+rR16cq0adO0evVqffzxxxo2bJgkKSsrSx4eHtZ+PTw8dOrUqWJrvl5BQYESExOtyz0GDBigpKQkTZ06VefOndM777yjWbNmWeusV6+e2rZtK0nFjuMfnT9/XnPmzFFiYqJ1Lfr8+fO1adMmLVy4UGPHjrW2nTp1qtq3by9Jeumll9S9e3ddunRJLi4uRfadm5ur3Nxc6+vs7OwS3zcAAKhYyiwIWywWm/9E/kfLly/XzJkzdfToUeXk5Cg/P19eXl42bYKCgqwhWJJat26tgoICpaamytPTU0ePHlVUVJSGDh1qbZOfny9vb+9iazp//vxNzxs0aJC6dOmiBg0aKDIyUj169FDXrl1t+hk/frz++c9/6tKlS/Lw8FB8fLy6d++usWPH6siRI4XW9166dElHjx5VVlaWMjMz1apVK+sxR0dHNW/evNDyiGtSU1Pl6Oio8PBw676QkBDdcccdhdpeC8rX7Nu3TykpKTYzwFeuXNGlS5d04cIF7du3Tzk5OapatarNeRcvXtTRo0etrz09PfXdd99ZXzs4lO4/HAQHB9u8JwEBAdYZ7UOHDik3N1edOnUqVZ9/dPToUeXl5alNmzbWfU5OTmrZsqUOHTpk0zYsLMymDkk6ffq0goKCiuw7Li5OkydPvuXaAABAxVFmQfjQoUPW9Z1/tGPHDvXv31+TJ09WRESEvL29tWzZMk2fPr3Efefk5Ei6Ouv3x1ApSVWqVPlT54WHh+v48eNav369Nm/erL59+6pz5876+OOPrW3Hjh2rQYMGycPDQzVq1LAG/pycHDVr1kxLly4tdO1rM5u3k7u7u83rnJwcTZ482Tr7/EcuLi7KyclRQECAkpOTCx3/49pfBwcHhYSEFGrj4OBQKMAXtdbbycnJ5rXJZLI+VcTV1bXY+7kd/ljLtXH74xNOrhcTE6NRo0ZZX2dnZyswMPD2FQgAAOymTILwf/7zHx04cEAvvvhioWPbt29X7dq19corr1j3nThxolC79PR0nTx5UjVr1pQkff3113JwcFCDBg1Uo0YN1axZU8eOHSv26QXOzs6Srs6AXlOS86SrT0no16+f+vXrp0cffVSRkZH6/fff5evrK0mqVq1akcEwPDxcy5cvV/Xq1QvNcF8TEBCgnTt3ql27dpKuzkZfW5tblAYNGig/P1979uxRs2bNJF1dP33mzJli6/9jPampqUXWeu34qVOn5OjoeMN10MXx8/PTuXPndP78eWsIL+3j6kJDQ+Xq6qqkpCQNGTKk0PGixvF69erVk7Ozs1JSUqxLWfLy8rR79+4//Txms9lcqZ54AgAAilfqIJybm6tTp07pypUr+uWXX7RhwwbFxcWpR48eevrppwu1Dw0NVXp6upYtW6YWLVroiy++KPLLYi4uLho4cKCmTZum7OxsRUdHq2/fvtZ1opMnT1Z0dLS8vb0VGRmp3NxcffPNNzpz5oxGjRql6tWry9XVVRs2bNCdd94pFxcXeXt73/S8t956SwEBAWratKkcHBy0cuVK+fv7l+hRXf3799ebb76pXr16acqUKbrzzjt14sQJffLJJxo3bpzuvPNOPf/884qPj1doaKgaNmyot956S2fPni22z4YNG6pz584aNmyY5syZIycnJ40ePVqurq7FLj25ZuLEierRo4eCgoL06KOPysHBQfv27dP333+v1157TZ07d1br1q3Vu3dvJSQkqH79+jp58qS++OILPfzww4WWWlyvVatWcnNz08svv6zo6Gjt3Lmz1M/2dXFx0fjx4zVu3Dg5OzurTZs2+vXXX/XDDz8oKiqq2HH8I3d3dw0fPlxjx46Vr6+vgoKClJCQoAsXLigqKqpU9QAAAOMq9VMjNmzYoICAAAUHBysyMlL//e9/NXPmTH322WdFLlN46KGH9OKLL2rkyJG65557tH37dk2YMKFQu5CQED3yyCN68MEH1bVrV4WFhdk85mzIkCFasGCBFi1apMaNG6t9+/ZKTEy0LsdwdHTUzJkzNW/ePNWsWVO9evUq0Xmenp5KSEhQ8+bN1aJFC6WlpWndunUlWhfr5uamLVu2KCgoSI888ogaNWqkqKgoXbp0yTpDPHr0aA0YMEADBw5U69at5enpqYcffviG/b7//vuqUaOG2rVrp4cfflhDhw6Vp6dnsV/wuiYiIkJr167Vl19+qRYtWujee+/VjBkzrLOmJpNJ69atU7t27fT3v/9d9evX1+OPP64TJ05Yn9pwI76+vlqyZInWrVunxo0b66OPPtKkSZNuet71JkyYoNGjR2vixIlq1KiR+vXrZ11DXNw4Xi8+Pl59+vTRgAEDFB4eriNHjmjjxo1FrqUGAAAoislS3Le2UG789NNPCgwM1ObNm//Ul8xQetnZ2fL29lbgCyvkYHazdzkA/iAtvru9SwBQTl37/M7Kyip2+apUhl+WQ9n5z3/+o5ycHDVu3FiZmZkaN26cgoODreuMAQAA8OcRhMuhvLw8vfzyyzp27Jg8PT113333aenSpYWexgAAAIBbRxAuhyIiIhQREWHvMgAAACq1MvmJZQAAAKCiIQgDAADAkFgaAZTA95MjbvitUwAAUPEwIwwAAABDIggDAADAkAjCAAAAMCSCMAAAAAyJIAwAAABDIggDAADAkAjCAAAAMCSCMAAAAAyJIAwAAABDIggDAADAkAjCAAAAMCSCMAAAAAyJIAwAAABDIggDAADAkAjCAAAAMCSCMAAAAAyJIAwAAABDIggDAADAkAjCAAAAMCSCMAAAAAyJIAwAAABDIggDAADAkBztXQBQEfwtdqMczG72LgNAOZIW393eJQD4k5gRBgAAgCERhAEAAGBIBGEAAAAYEkEYAAAAhkQQBgAAgCERhAEAAGBIBGEAAAAYEkEY5daOHTtUpUoVde/OszoBAEDZIwij3Fq4cKGee+45bdmyRSdPnrR3OQAAoJIhCKNcysnJ0fLlyzV8+HB1795diYmJNsfXrFmj0NBQubi4qGPHjlq8eLFMJpPOnj1rbbNt2zbdf//9cnV1VWBgoKKjo3X+/Pm/9kYAAEC5RRBGubRixQo1bNhQDRo00FNPPaX33ntPFotFknT8+HE9+uij6t27t/bt26dnnnlGr7zyis35R48eVWRkpPr06aP9+/dr+fLl2rZtm0aOHHnD6+bm5io7O9tmAwAAlRNBGOXSwoUL9dRTT0mSIiMjlZWVpa+++kqSNG/ePDVo0EBvvvmmGjRooMcff1yDBg2yOT8uLk79+/fXCy+8oNDQUN13332aOXOm3n//fV26dKnY68bFxcnb29u6BQYG3rZ7BAAA9kUQRrmTmpqqXbt26YknnpAkOTo6ql+/flq4cKH1eIsWLWzOadmypc3rffv2KTExUR4eHtYtIiJCBQUFOn78eLHXjomJUVZWlnXLyMgo47sDAADlhaO9CwCut3DhQuXn56tmzZrWfRaLRWazWbNmzSpRHzk5OXrmmWcUHR1d6FhQUFCx55nNZpnN5tIXDQAAKhyCMMqV/Px8vf/++5o+fbq6du1qc6x379766KOP1KBBA61bt87m2O7du21eh4eH6+DBgwoJCbntNQMAgIqJIIxyZe3atTpz5oyioqLk7e1tc6xPnz5auHChVqxYobfeekvjx49XVFSU9u7da32qhMlkkiSNHz9e9957r0aOHKkhQ4bI3d1dBw8e1KZNm0o8qwwAACo31gijXFm4cKE6d+5cKARLV4PwN998o3Pnzunjjz/WJ598orCwMM2ZM8f61IhryxrCwsL01Vdf6f/+7/90//33q2nTppo4caLNcgsAAGBsJsu1Z1IBFdjUqVM1d+7cMv9yW3Z29tWnR7ywQg5mtzLtG0DFlhbPr14C5dW1z++srCx5eXkV246lEaiQZs+erRYtWqhq1apKSUnRm2++edNnBAMAAPwRQRgV0uHDh/Xaa6/p999/V1BQkEaPHq2YmBh7lwUAACoQgjAqpBkzZmjGjBn2LgMAAFRgfFkOAAAAhsSMMFAC30+OuOFiewAAUPEwIwwAAABDIggDAADAkAjCAAAAMCSCMAAAAAyJIAwAAABDIggDAADAkAjCAAAAMCSCMAAAAAyJIAwAAABDIggDAADAkAjCAAAAMCSCMAAAAAyJIAwAAABDIggDAADAkAjCAAAAMCSCMAAAAAyJIAwAAABDIggDAADAkAjCAAAAMCSCMAAAAAyJIAwAAABDIggDAADAkBztXQBQEfwtdqMczG72LgNABZYW393eJQC4DjPCAAAAMCSCMAAAAAyJIAwAAABDIggDAADAkAjCAAAAMCSCMAAAAAyJIAwAAABDIgij3Bo0aJBMJpN1q1q1qiIjI7V//35rm2vHvv76a5tzc3NzVbVqVZlMJiUnJ9u0X7169V90BwAAoDwjCKNci4yMVGZmpjIzM5WUlCRHR0f16NHDpk1gYKAWLVpks+/TTz+Vh4fHX1kqAACoYAjCKNfMZrP8/f3l7++ve+65Ry+99JIyMjL066+/WtsMHDhQy5Yt08WLF6373nvvPQ0cONAeJQMAgAqCIIwKIycnR0uWLFFISIiqVq1q3d+sWTMFBwdr1apVkqT09HRt2bJFAwYMKPU1cnNzlZ2dbbMBAIDKiSCMcm3t2rXy8PCQh4eHPD09tWbNGi1fvlwODrb/6A4ePFjvvfeeJCkxMVEPPvig/Pz8Sn29uLg4eXt7W7fAwMAyuQ8AAFD+EIRRrnXs2FF79+7V3r17tWvXLkVERKhbt246ceKETbunnnpKO3bs0LFjx5SYmKjBgwff0vViYmKUlZVl3TIyMsriNgAAQDlEEEa55u7urpCQEIWEhKhFixZasGCBzp8/r/nz59u0q1q1qnr06KGoqChdunRJ3bp1u6Xrmc1meXl52WwAAKByIgijQjGZTHJwcLD5Ytw1gwcPVnJysp5++mlVqVLFDtUBAICKxNHeBQA3kpubq1OnTkmSzpw5o1mzZiknJ0c9e/Ys1DYyMlK//vors7gAAKBECMIo1zZs2KCAgABJkqenpxo2bKiVK1eqQ4cOhdqaTCZVq1btL64QAABUVARhlFuJiYlKTEy8YRuLxVLsMR8fn0LHb9QeAAAYC2uEAQAAYEgEYQAAABgSQRgAAACGRBAGAACAIRGEAQAAYEg8NQIoge8nR/B8YgAAKhlmhAEAAGBIBGEAAAAYEkEYAAAAhkQQBgAAgCERhAEAAGBIBGEAAAAYEkEYAAAAhkQQBgAAgCERhAEAAGBIBGEAAAAYEkEYAAAAhkQQBgAAgCERhAEAAGBIBGEAAAAYEkEYAAAAhkQQBgAAgCERhAEAAGBIBGEAAAAYEkEYAAAAhkQQBgAAgCERhAEAAGBIBGEAAAAYkqO9CwAqgr/FbpSD2c3eZQAAUGmkxXe3dwnMCAMAAMCYCMIAAAAwJIIwAAAADIkgDAAAAEMiCAMAAMCQCMIoUyaTSatXr7Z3GQAAADdFEEaJnTp1Ss8//7xCQkLk4uKiGjVqqE2bNpozZ44uXLhg7/IAAABKhecIo0SOHTumNm3ayMfHR6+//roaN24ss9msAwcO6N///rdq1aqlhx56yN5lAgAAlBgzwiiRZ599Vo6Ojvrmm2/Ut29fNWrUSHXr1lWvXr30xRdfqGfPnoXOSU5Olslk0tmzZ6379u7dK5PJpLS0NOu+lJQUdejQQW5ubrrjjjsUERGhM2fOSJJyc3MVHR2t6tWry8XFRW3bttXu3but5545c0b9+/eXn5+fXF1dFRoaqkWLFlmPZ2RkqG/fvvLx8ZGvr6969eplc20AAGBcBGHc1G+//aYvv/xSI0aMkLu7e5FtTCbTLfW9d+9ederUSXfddZd27Nihbdu2qWfPnrpy5Yokady4cVq1apUWL16s7777TiEhIYqIiNDvv/8uSZowYYIOHjyo9evX69ChQ5ozZ46qVasmScrLy1NERIQ8PT21detWpaSkyMPDQ5GRkbp8+XKR9eTm5io7O9tmAwAAlRNLI3BTR44ckcViUYMGDWz2V6tWTZcuXZIkjRgxQm+88Uap+05ISFDz5s01e/Zs6767775bknT+/HnNmTNHiYmJ6tatmyRp/vz52rRpkxYuXKixY8cqPT1dTZs2VfPmzSVJwcHB1n6WL1+ugoICLViwwBrUFy1aJB8fHyUnJ6tr166F6omLi9PkyZNLfR8AAKDiYUYYt2zXrl3au3ev7r77buXm5t5SH9dmhIty9OhR5eXlqU2bNtZ9Tk5OatmypQ4dOiRJGj58uJYtW6Z77rlH48aN0/bt261t9+3bpyNHjsjT01MeHh7y8PCQr6+vLl26pKNHjxZ5zZiYGGVlZVm3jIyMW7ovAABQ/jEjjJsKCQmRyWRSamqqzf66detKklxdXYs8z8Hh6r9nWSwW6768vDybNsWdW1LdunXTiRMntG7dOm3atEmdOnXSiBEjNG3aNOXk5KhZs2ZaunRpofP8/PyK7M9sNstsNv+pmgAAQMXAjDBuqmrVqurSpYtmzZql8+fPl/i8a2EzMzPTum/v3r02bcLCwpSUlFTk+fXq1ZOzs7NSUlKs+/Ly8rR7927dddddNtcZOHCglixZorffflv//ve/JUnh4eE6fPiwqlevrpCQEJvN29u7xPcBAAAqJ4IwSmT27NnKz89X8+bNtXz5ch06dEipqalasmSJfvzxR1WpUqXQOSEhIQoMDNSkSZN0+PBhffHFF5o+fbpNm5iYGO3evVvPPvus9u/frx9//FFz5szR//73P7m7u2v48OEaO3asNmzYoIMHD2ro0KG6cOGCoqKiJEkTJ07UZ599piNHjuiHH37Q2rVr1ahRI0lS//79Va1aNfXq1Utbt27V8ePHlZycrOjoaP3000+3/00DAADlGkEYJVKvXj3t2bNHnTt3VkxMjJo0aaLmzZvr3Xff1ZgxY/Tqq68WOsfJyUkfffSRfvzxR4WFhemNN97Qa6+9ZtOmfv36+vLLL7Vv3z61bNlSrVu31meffSZHx6urduLj49WnTx8NGDBA4eHhOnLkiDZu3Kg77rhDkuTs7KyYmBiFhYWpXbt2qlKlipYtWyZJcnNz05YtWxQUFKRHHnlEjRo1UlRUlC5duiQvL6/b/I4BAIDyzmT54wJOADays7Pl7e2twBdWyMHsZu9yAACoNNLiu9+2vq99fmdlZd1w8osZYQAAABgSQRgAAACGRBAGAACAIRGEAQAAYEgEYQAAABgSvywHlMD3kyN45BoAAJUMM8IAAAAwJIIwAAAADIkgDAAAAEMiCAMAAMCQCMIAAAAwJIIwAAAADIkgDAAAAEMiCAMAAMCQCMIAAAAwJH5ZDrgBi8UiScrOzrZzJQAAoKSufW5f+xwvDkEYuIHffvtNkhQYGGjnSgAAQGmdO3dO3t7exR4nCAM34OvrK0lKT0+/4V8k/DWys7MVGBiojIwMeXl52bscw2M8yhfGo/xgLOzPYrHo3Llzqlmz5g3bEYSBG3BwuLqM3tvbm/8zK0e8vLwYj3KE8ShfGI/yg7Gwr5JMYPFlOQAAABgSQRgAAACGRBAGbsBsNis2NlZms9nepUCMR3nDeJQvjEf5wVhUHCbLzZ4rAQAAAFRCzAgDAADAkAjCAAAAMCSCMAAAAAyJIAwAAABDIgjDcP71r38pODhYLi4uatWqlXbt2nXD9itXrlTDhg3l4uKixo0ba926dTbHLRaLJk6cqICAALm6uqpz5846fPjw7byFSqOsx2LQoEEymUw2W2Rk5O28hUqlNOPxww8/qE+fPgoODpbJZNLbb7/9p/uErbIej0mTJhX6+9GwYcPbeAeVS2nGY/78+br//vt1xx136I477lDnzp0Lteezo3wgCMNQli9frlGjRik2NlbfffedmjRpooiICJ0+fbrI9tu3b9cTTzyhqKgo7dmzR71791bv3r31/fffW9skJCRo5syZmjt3rnbu3Cl3d3dFRETo0qVLf9VtVUi3YywkKTIyUpmZmdbto48++itup8Ir7XhcuHBBdevWVXx8vPz9/cukT/w/t2M8JOnuu++2+fuxbdu223ULlUppxyM5OVlPPPGE/vvf/2rHjh0KDAxU165d9fPPP1vb8NlRTlgAA2nZsqVlxIgR1tdXrlyx1KxZ0xIXF1dk+759+1q6d+9us69Vq1aWZ555xmKxWCwFBQUWf39/y5tvvmk9fvbsWYvZbLZ89NFHt+EOKo+yHguLxWIZOHCgpVevXrel3squtOPxR7Vr17bMmDGjTPs0utsxHrGxsZYmTZqUYZXG8Wf/Wc7Pz7d4enpaFi9ebLFY+OwoT5gRhmFcvnxZ3377rTp37mzd5+DgoM6dO2vHjh1FnrNjxw6b9pIUERFhbX/8+HGdOnXKpo23t7datWpVbJ+4PWNxTXJysqpXr64GDRpo+PDh+u2338r+BiqZWxkPe/RpFLfzvTt8+LBq1qypunXrqn///kpPT/+z5VZ6ZTEeFy5cUF5ennx9fSXx2VGeEIRhGP/73/905coV1ahRw2Z/jRo1dOrUqSLPOXXq1A3bX/vf0vSJ2zMW0tVlEe+//76SkpL0xhtv6KuvvlK3bt105cqVsr+JSuRWxsMefRrF7XrvWrVqpcTERG3YsEFz5szR8ePHdf/99+vcuXN/tuRKrSzGY/z48apZs6Y1+PLZUX442rsAACgrjz/+uPXPjRs3VlhYmOrVq6fk5GR16tTJjpUB9tetWzfrn8PCwtSqVSvVrl1bK1asUFRUlB0rq9zi4+O1bNkyJScny8XFxd7l4DrMCMMwqlWrpipVquiXX36x2f/LL78U++USf3//G7a/9r+l6RO3ZyyKUrduXVWrVk1Hjhz580VXYrcyHvbo0yj+qvfOx8dH9evX5+/HTfyZ8Zg2bZri4+P15ZdfKiwszLqfz47ygyAMw3B2dlazZs2UlJRk3VdQUKCkpCS1bt26yHNat25t016SNm3aZG1fp04d+fv727TJzs7Wzp07i+0Tt2csivLTTz/pt99+U0BAQNkUXkndynjYo0+j+Kveu5ycHB09epS/Hzdxq+ORkJCgV199VRs2bFDz5s1tjvHZUY7Y+9t6wF9p2bJlFrPZbElMTLQcPHjQMmzYMIuPj4/l1KlTFovFYhkwYIDlpZdesrZPSUmxODo6WqZNm2Y5dOiQJTY21uLk5GQ5cOCAtU18fLzFx8fH8tlnn1n2799v6dWrl6VOnTqWixcv/uX3V5GU9VicO3fOMmbMGMuOHTssx48ft2zevNkSHh5uCQ0NtVy6dMku91iRlHY8cnNzLXv27LHs2bPHEhAQYBkzZoxlz549lsOHD5e4TxTvdozH6NGjLcnJyZbjx49bUlJSLJ07d7ZUq1bNcvr06b/8/iqa0o5HfHy8xdnZ2fLxxx9bMjMzrdu5c+ds2vDZYX8EYRjOu+++awkKCrI4OztbWrZsafn666+tx9q3b28ZOHCgTfsVK1ZY6tevb3F2drbcfffdli+++MLmeEFBgWXChAmWGjVqWMxms6VTp06W1NTUv+JWKryyHIsLFy5YunbtavHz87M4OTlZateubRk6dCihqxRKMx7Hjx+3SCq0tW/fvsR94sbKejz69etnCQgIsDg7O1tq1apl6devn+XIkSN/4R1VbKUZj9q1axc5HrGxsdY2fHaUDyaLxWKxw0Q0AAAAYFesEQYAAIAhEYQBAABgSARhAAAAGBJBGAAAAIZEEAYAAIAhEYQBAABgSARhAAAAGBJBGAAAAIZEEAYAAIAhEYQBAABgSARhAAAAGBJBGAAAAIb0/wGKtudWST9JjQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "\n",
    "# Split the dataset into X and y\n",
    "X = dataset.drop(columns=[\"Outcome\"])\n",
    "y = dataset[\"Outcome\"]\n",
    "\n",
    "# initialize ExtraTreesClassifier\n",
    "model = ExtraTreesClassifier()\n",
    "\n",
    "# fit the model to your dataset\n",
    "model.fit(X, y)\n",
    "\n",
    "# plot feature importances\n",
    "feat_importances = pd.Series(model.feature_importances_, index=X.columns)\n",
    "feat_importances.nlargest(15).plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "n9h7wDxDgY08",
   "metadata": {
    "id": "n9h7wDxDgY08"
   },
   "source": [
    "### Using SelectKBest to get more efficient feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3e2500a4",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1686345157052,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "3e2500a4",
    "outputId": "d9a44bbe-d938-4b55-d1a8-ad41ffa891c7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    Feature        Score\n",
      "0                   Glucose  1412.839409\n",
      "1                   Insulin  1268.739094\n",
      "2                       Age   182.480280\n",
      "3                       BMI   120.127438\n",
      "4               Pregnancies   111.027046\n",
      "5             SkinThickness    92.046303\n",
      "6             BloodPressure    25.955584\n",
      "7  DiabetesPedigreeFunction     4.911958\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from sklearn.feature_selection import SelectKBest, chi2\n",
    "\n",
    "# Split the dataset into X and y\n",
    "X = dataset.drop(columns=[\"Outcome\"])\n",
    "y = dataset[\"Outcome\"]\n",
    "\n",
    "# Select the top 10 features with the highest ANOVA F-value\n",
    "k = 8\n",
    "selector = SelectKBest(score_func=chi2, k=k)\n",
    "X_new = selector.fit_transform(X, y)\n",
    "\n",
    "# Define feature names\n",
    "feature_names = list(X.columns)\n",
    "\n",
    "# Create a pandas dataframe of feature scores\n",
    "scores_df = pd.DataFrame({'Feature': feature_names, 'Score': selector.scores_})\n",
    "\n",
    "# Sort the dataframe by feature scores in descending order\n",
    "scores_df = scores_df.sort_values('Score', ascending=False).reset_index(drop=True)\n",
    "\n",
    "# Print the dataframe\n",
    "print(scores_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "xd7qMTrEgc4O",
   "metadata": {
    "id": "xd7qMTrEgc4O"
   },
   "source": [
    "### Combining SelectKBest with ExtraTreesClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1e49c34e",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "executionInfo": {
     "elapsed": 1370,
     "status": "ok",
     "timestamp": 1686345158418,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "1e49c34e",
    "outputId": "4acb91bf-9322-45b4-848c-efaac885c595"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtYAAAHHCAYAAACbch9lAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABWs0lEQVR4nO3deVwVZf//8fdB9l3cQENcwKUSE7fMPRdwS9vUMgPXO9PM3Iq6XdAUMk1Tb5fURMtSy24zc0ktMpdccs3tdsG0xDQXEAlEmN8f/jjfjoAJjSL4ej4e84gzc801nzmjnbcX18yxGIZhCAAAAMA/YlfQBQAAAABFAcEaAAAAMAHBGgAAADABwRoAAAAwAcEaAAAAMAHBGgAAADABwRoAAAAwAcEaAAAAMAHBGgAAADABwRoAAAAwAcEaAO5TsbGxslgsOS5vvPHGHTnmli1bNHr0aF2+fPmO9P9PZL0fO3fuLOhS8m3GjBmKjY0t6DKA+5Z9QRcAAChYY8aMUcWKFW3WPfzww3fkWFu2bFFUVJQiIiLk7e19R45xP5sxY4ZKliypiIiIgi4FuC8RrAHgPtemTRvVqVOnoMv4R65evSo3N7eCLqPApKSkyNXVtaDLAO57TAUBANzS6tWr1bhxY7m5ucnDw0Pt2rXTgQMHbNrs27dPERERqlSpkpydneXr66uePXvqwoUL1jajR4/WsGHDJEkVK1a0Tjs5efKkTp48KYvFkuM0BovFotGjR9v0Y7FYdPDgQT3//PMqXry4GjVqZN3+8ccfq3bt2nJxcZGPj4+6du2q06dP5+vcIyIi5O7urlOnTql9+/Zyd3dXuXLl9J///EeStH//fj3++ONyc3NTQECAPvnkE5v9s6aXbNy4Uf/6179UokQJeXp66sUXX9SlS5eyHW/GjBl66KGH5OTkpLJly6p///7Zps00a9ZMDz/8sH766Sc1adJErq6uevPNN1WhQgUdOHBA33//vfW9bdasmSTp4sWLGjp0qGrUqCF3d3d5enqqTZs22rt3r03fcXFxslgsWrp0qcaNG6cHHnhAzs7OatGihY4dO5at3m3btqlt27YqXry43NzcFBwcrPfff9+mzeHDh/XMM8/Ix8dHzs7OqlOnjlasWGHTJj09XVFRUQoKCpKzs7NKlCihRo0aad26dbd1nYB7BSPWAHCfS0xM1B9//GGzrmTJkpKkjz76SOHh4QoNDdU777yjlJQUzZw5U40aNdLu3btVoUIFSdK6det04sQJ9ejRQ76+vjpw4IA++OADHThwQD/++KMsFoueeuop/e9//9Onn36qyZMnW49RqlQpnT9/Ps91P/vsswoKCtL48eNlGIYkady4cRoxYoQ6d+6s3r176/z585o2bZqaNGmi3bt352v6SUZGhtq0aaMmTZpowoQJWrRokQYMGCA3Nze99dZb6tatm5566inNmjVLL774oho0aJBtas2AAQPk7e2t0aNH68iRI5o5c6Z++eUXa5CVbvyDISoqSi1btlS/fv2s7Xbs2KHNmzfLwcHB2t+FCxfUpk0bde3aVS+88ILKlCmjZs2a6ZVXXpG7u7veeustSVKZMmUkSSdOnNDy5cv17LPPqmLFivr99981e/ZsNW3aVAcPHlTZsmVt6o2JiZGdnZ2GDh2qxMRETZgwQd26ddO2bdusbdatW6f27dvLz89Pr776qnx9fXXo0CGtXLlSr776qiTpwIEDatiwocqVK6c33nhDbm5uWrp0qTp16qRly5bpySeftJ57dHS0evfurXr16ikpKUk7d+7Url271KpVqzxfM6DAGACA+9L8+fMNSTkuhmEYV65cMby9vY0+ffrY7Hf27FnDy8vLZn1KSkq2/j/99FNDkrFx40brunfffdeQZMTHx9u0jY+PNyQZ8+fPz9aPJGPUqFHW16NGjTIkGc8995xNu5MnTxrFihUzxo0bZ7N+//79hr29fbb1ub0fO3bssK4LDw83JBnjx4+3rrt06ZLh4uJiWCwWY/Hixdb1hw8fzlZrVp+1a9c2rl27Zl0/YcIEQ5Lx5ZdfGoZhGOfOnTMcHR2N1q1bGxkZGdZ206dPNyQZH374oXVd06ZNDUnGrFmzsp3DQw89ZDRt2jTb+tTUVJt+DePGe+7k5GSMGTPGuu67774zJBnVq1c30tLSrOvff/99Q5Kxf/9+wzAM4/r160bFihWNgIAA49KlSzb9ZmZmWn9u0aKFUaNGDSM1NdVm+2OPPWYEBQVZ19WsWdNo165dtrqBwoapIABwn/vPf/6jdevW2SzSjRHJy5cv67nnntMff/xhXYoVK6b69evru+++s/bh4uJi/Tk1NVV//PGHHn30UUnSrl277kjdL730ks3rL774QpmZmercubNNvb6+vgoKCrKpN6969+5t/dnb21tVq1aVm5ubOnfubF1ftWpVeXt768SJE9n279u3r82Ic79+/WRvb69Vq1ZJktavX69r165p0KBBsrP7v4/mPn36yNPTU19//bVNf05OTurRo8dt1+/k5GTtNyMjQxcuXJC7u7uqVq2a4/Xp0aOHHB0dra8bN24sSdZz2717t+Lj4zVo0KBsvwXIGoG/ePGivv32W3Xu3FlXrlyxXo8LFy4oNDRUR48e1W+//Sbpxnt64MABHT169LbPCbgXMRUEAO5z9erVy/HmxayQ8/jjj+e4n6enp/XnixcvKioqSosXL9a5c+ds2iUmJppY7f+5ebrF0aNHZRiGgoKCcmz/12CbF87OzipVqpTNOi8vLz3wwAPWEPnX9TnNnb65Jnd3d/n5+enkyZOSpF9++UXSjXD+V46OjqpUqZJ1e5Zy5crZBN+/k5mZqffff18zZsxQfHy8MjIyrNtKlCiRrX358uVtXhcvXlySrOd2/PhxSbd+esyxY8dkGIZGjBihESNG5Njm3LlzKleunMaMGaOOHTuqSpUqevjhhxUWFqbu3bsrODj4ts8RuBcQrAEAOcrMzJR0Y561r69vtu329v/3EdK5c2dt2bJFw4YN0yOPPCJ3d3dlZmYqLCzM2s+t3BxQs/w1AN7sr6PkWfVaLBatXr1axYoVy9be3d39b+vISU593Wq98f/ne99JN5/73xk/frxGjBihnj17auzYsfLx8ZGdnZ0GDRqU4/Ux49yy+h06dKhCQ0NzbBMYGChJatKkiY4fP64vv/xS33zzjebOnavJkydr1qxZNr8tAO51BGsAQI4qV64sSSpdurRatmyZa7tLly5pw4YNioqK0siRI63rc/q1fm4BOmtE9OYnYNw8Uvt39RqGoYoVK6pKlSq3vd/dcPToUTVv3tz6Ojk5WQkJCWrbtq0kKSAgQJJ05MgRVapUydru2rVrio+Pv+X7/1e5vb+ff/65mjdvrnnz5tmsv3z5svUm0rzI+rPx888/51pb1nk4ODjcVv0+Pj7q0aOHevTooeTkZDVp0kSjR48mWKNQYY41ACBHoaGh8vT01Pjx45Wenp5te9aTPLJGN28ezZwyZUq2fbKeNX1zgPb09FTJkiW1ceNGm/UzZsy47XqfeuopFStWTFFRUdlqMQzD5tF/d9sHH3xg8x7OnDlT169fV5s2bSRJLVu2lKOjo6ZOnWpT+7x585SYmKh27drd1nHc3Nxy/FbLYsWKZXtPPvvsM+sc57wKCQlRxYoVNWXKlGzHyzpO6dKl1axZM82ePVsJCQnZ+vjrk2Buvjbu7u4KDAxUWlpavuoDCgoj1gCAHHl6emrmzJnq3r27QkJC1LVrV5UqVUqnTp3S119/rYYNG2r69Ony9PS0PoouPT1d5cqV0zfffKP4+PhsfdauXVuS9NZbb6lr165ycHBQhw4d5Obmpt69eysmJka9e/dWnTp1tHHjRv3vf/+77XorV66st99+W5GRkTp58qQ6deokDw8PxcfH67///a/69u2roUOHmvb+5MW1a9fUokULde7cWUeOHNGMGTPUqFEjPfHEE5JuPHIwMjJSUVFRCgsL0xNPPGFtV7duXb3wwgu3dZzatWtr5syZevvttxUYGKjSpUvr8ccfV/v27TVmzBj16NFDjz32mPbv369FixbZjI7nhZ2dnWbOnKkOHTrokUceUY8ePeTn56fDhw/rwIEDWrt2raQbN8Y2atRINWrUUJ8+fVSpUiX9/vvv2rp1q3799Vfrc7QffPBBNWvWTLVr15aPj4927typzz//XAMGDMhXfUCBKaCnkQAAClhOj5fLyXfffWeEhoYaXl5ehrOzs1G5cmUjIiLC2Llzp7XNr7/+ajz55JOGt7e34eXlZTz77LPGmTNnsj1+zjAMY+zYsUa5cuUMOzs7m0fvpaSkGL169TK8vLwMDw8Po3Pnzsa5c+dyfdze+fPnc6x32bJlRqNGjQw3NzfDzc3NqFatmtG/f3/jyJEjeX4/wsPDDTc3t2xtmzZtajz00EPZ1gcEBNg8Ni6rz++//97o27evUbx4ccPd3d3o1q2bceHChWz7T58+3ahWrZrh4OBglClTxujXr1+2x9nldmzDuPEoxHbt2hkeHh6GJOuj91JTU40hQ4YYfn5+houLi9GwYUNj69atRtOmTW0ez5f1uL3PPvvMpt/cHoe4adMmo1WrVoaHh4fh5uZmBAcHG9OmTbNpc/z4cePFF180fH19DQcHB6NcuXJG+/btjc8//9za5u233zbq1atneHt7Gy4uLka1atWMcePG2TyiECgMLIZxF+6yAADgPhQbG6sePXpox44dhf5r4wH8PeZYAwAAACYgWAMAAAAmIFgDAAAAJmCONQAAAGACRqwBAAAAExCsAQAAABPwBTHAXZSZmakzZ87Iw8Mj168eBgAA9xbDMHTlyhWVLVtWdna5j0sTrIG76MyZM/L39y/oMgAAQD6cPn1aDzzwQK7bCdbAXeTh4SHpxl9MT0/PAq4GAADcjqSkJPn7+1s/x3NDsAbuoqzpH56engRrAAAKmb+bxsnNiwAAAIAJCNYAAACACQjWAAAAgAkI1gAAAIAJCNYAAACACQjWAAAAgAkI1gAAAIAJCNYAAACACQjWAAAAgAn45kWgADw8aq3snFwLugzglk7GtCvoEgCgUGHEGgAAADABwRoAAAAwAcEaAAAAMAHBGgAAADABwRoAAAAwAcH6PmKxWLR8+fJct1eoUEFTpkwx9ZjNmjXToEGD/lFdfzV69Gg98sgj/7guAAAAsxGsi5Dz58+rX79+Kl++vJycnOTr66vQ0FBt3rz5tvbfsWOH+vbte1ttR48eLYvFcsvldiUkJKhNmza33R4AAOBexHOsi5Cnn35a165d04IFC1SpUiX9/vvv2rBhgy5cuHBb+5cqVeq2jzV06FC99NJL1td169ZV37591adPnzzX7evrm+d9AAAA7jWMWBcRly9f1g8//KB33nlHzZs3V0BAgOrVq6fIyEg98cQTOe4zatQo+fn5ad++fZKyTwWxWCyaO3eunnzySbm6uiooKEgrVqyQJLm7u8vX19e6FCtWTB4eHjbrsmRmZmr48OHy8fGRr6+vRo8ebVPHzVNBfv31Vz333HPy8fGRm5ub6tSpo23btuV4DsePH1elSpU0YMAAGYah2NhYeXt7a+3atapevbrc3d0VFhamhIQEm/3mzp2r6tWry9nZWdWqVdOMGTOs265du6YBAwbIz89Pzs7OCggIUHR0tCTJMAyNHj3a+luBsmXLauDAgbe+OAAA4L5AsC4i3N3d5e7uruXLlystLe2WbQ3D0CuvvKKFCxfqhx9+UHBwcK5to6Ki1LlzZ+3bt09t27ZVt27ddPHixTzVtmDBArm5uWnbtm2aMGGCxowZo3Xr1uXYNjk5WU2bNtVvv/2mFStWaO/evRo+fLgyMzOztd23b58aNWqk559/XtOnT7dOP0lJSdHEiRP10UcfaePGjTp16pSGDh1q3W/RokUaOXKkxo0bp0OHDmn8+PEaMWKEFixYIEmaOnWqVqxYoaVLl+rIkSNatGiRKlSoIElatmyZJk+erNmzZ+vo0aNavny5atSokeu5p6WlKSkpyWYBAABFE1NBigh7e3vFxsaqT58+mjVrlkJCQtS0aVN17drVJjhfv35dL7zwgnbv3q1NmzapXLlyt+w3IiJCzz33nCRp/Pjxmjp1qrZv366wsLDbri04OFijRo2SJAUFBWn69OnasGGDWrVqla3tJ598ovPnz2vHjh3y8fGRJAUGBmZrt2XLFrVv315vvfWWhgwZYrMtPT1ds2bNUuXKlSVJAwYM0JgxY6zbR40apUmTJumpp56SJFWsWFEHDx7U7NmzFR4erlOnTikoKEiNGjWSxWJRQECAdd9Tp07J19dXLVu2lIODg8qXL6969erleu7R0dGKioq63bcKAAAUYoxYFyFPP/20zpw5oxUrVigsLExxcXEKCQlRbGystc1rr72mbdu2aePGjX8bqiXZhHI3Nzd5enrq3Llzearr5hFxPz+/XPvYs2ePatWqZQ3VOTl16pRatWqlkSNHZgvVkuTq6moN1Tcf7+rVqzp+/Lh69eplHeV3d3fX22+/rePHj0u68Y+JPXv2qGrVqho4cKC++eYba1/PPvus/vzzT1WqVEl9+vTRf//7X12/fj3XWiMjI5WYmGhdTp8+nWtbAABQuBGsixhnZ2e1atVKI0aM0JYtWxQREWEdLZakVq1a6bffftPatWtvqz8HBweb1xaLJcdpGWb14eLi8rf9lSpVSvXq1dOnn36a49SKnI5nGIakG1NNJGnOnDnas2ePdfn555/1448/SpJCQkIUHx+vsWPH6s8//1Tnzp31zDPPSJL8/f115MgRzZgxQy4uLnr55ZfVpEkTpaen51irk5OTPD09bRYAAFA0EayLuAcffFBXr161vn7iiSf0ySefqHfv3lq8eHEBVpaz4OBg7dmz55bzuF1cXLRy5Uo5OzsrNDRUV65cue3+y5Qpo7Jly+rEiRMKDAy0WSpWrGht5+npqS5dumjOnDlasmSJli1bZq3JxcVFHTp00NSpUxUXF6etW7dq//79+T9pAABQJDDHuoi4cOGCnn32WfXs2VPBwcHy8PDQzp07NWHCBHXs2NGm7ZNPPqmPPvpI3bt3l729vXU09l7w3HPPafz48erUqZOio6Pl5+en3bt3q2zZsmrQoIG1nZubm77++mu1adNGbdq00Zo1a+Tu7n5bx4iKitLAgQPl5eWlsLAwpaWlaefOnbp06ZIGDx6s9957T35+fqpVq5bs7Oz02WefydfXV97e3oqNjVVGRobq168vV1dXffzxx3JxcbGZhw0AAO5PBOsiwt3dXfXr19fkyZN1/Phxpaeny9/fX3369NGbb76Zrf0zzzyjzMxMde/eXXZ2dtYb+Qqao6OjvvnmGw0ZMkRt27bV9evX9eCDD+o///lPtrbu7u5avXq1QkND1a5dO61ateq2jtG7d2+5urrq3Xff1bBhw+Tm5qYaNWpYvyHSw8NDEyZM0NGjR1WsWDHVrVtXq1atkp2dnby9vRUTE6PBgwcrIyNDNWrU0FdffaUSJUqY+TYAAIBCyGJkTT4FcMclJSXJy8tL/oOWys7JtaDLAW7pZEy7gi4BAO4JWZ/fiYmJt7xfijnWAAAAgAkI1gAAAIAJCNYAAACACQjWAAAAgAkI1gAAAIAJeNweUAB+jgrlWxgBAChiGLEGAAAATECwBgAAAExAsAYAAABMQLAGAAAATECwBgAAAExAsAYAAABMQLAGAAAATECwBgAAAExAsAYAAABMQLAGAAAATECwBgAAAExAsAYAAABMQLAGAAAATECwBgAAAExAsAYAAABMQLAGAAAATECwBgAAAExAsAYAAABMQLAGAAAATECwBgAAAExAsAYAAABMQLAGAAAATGBf0AUA96OHR62VnZNrQZcB3LaTMe0KugQAuOcxYg0AAACYgGANAAAAmIBgDQAAAJiAYA0AAACYgGANAAAAmIBgDUg6efKkLBaL9uzZI0mKi4uTxWLR5cuXC7QuAABQeBCscc+KiIhQp06dCuTYjz32mBISEuTl5VUgxwcAAIUPz7EGcuDo6ChfX9+CLgMAABQijFijUGjWrJkGDhyo4cOHy8fHR76+vho9erR1u2EYGj16tMqXLy8nJyeVLVtWAwcOtG63WCxavny5TZ/e3t6KjY3N8Xg3TwWJjY2Vt7e31q5dq+rVq8vd3V1hYWFKSEgw+UwBAEBhRbBGobFgwQK5ublp27ZtmjBhgsaMGaN169ZJkpYtW6bJkydr9uzZOnr0qJYvX64aNWqYevyUlBRNnDhRH330kTZu3KhTp05p6NCht9wnLS1NSUlJNgsAACiamAqCQiM4OFijRo2SJAUFBWn69OnasGGDWrVqpVOnTsnX11ctW7aUg4ODypcvr3r16pl6/PT0dM2aNUuVK1eWJA0YMEBjxoy55T7R0dGKiooytQ4AAHBvYsQahUZwcLDNaz8/P507d06S9Oyzz+rPP/9UpUqV1KdPH/33v//V9evXTT2+q6urNVTffPzcREZGKjEx0bqcPn3a1JoAAMC9g2CNQsPBwcHmtcViUWZmpiTJ399fR44c0YwZM+Ti4qKXX35ZTZo0UXp6urWtYRg2+2dt+yfHv7nPmzk5OcnT09NmAQAARRPBGkWGi4uLOnTooKlTpyouLk5bt27V/v37JUmlSpWyudHw6NGjSklJKahSAQBAEcQcaxQJsbGxysjIUP369eXq6qqPP/5YLi4uCggIkCQ9/vjjmj59uho0aKCMjAy9/vrr2UagAQAA/glGrFEkeHt7a86cOWrYsKGCg4O1fv16ffXVVypRooQkadKkSfL391fjxo31/PPPa+jQoXJ1dS3gqgEAQFFiMf5ukigA0yQlJcnLy0v+g5bKzolgj8LjZEy7gi4BAApM1ud3YmLiLe+XYsQaAAAAMAHBGgAAADABwRoAAAAwAcEaAAAAMAHBGgAAADABz7EGCsDPUaF8CyMAAEUMI9YAAACACQjWAAAAgAkI1gAAAIAJCNYAAACACQjWAAAAgAkI1gAAAIAJCNYAAACACQjWAAAAgAkI1gAAAIAJCNYAAACACQjWAAAAgAkI1gAAAIAJCNYAAACACQjWAAAAgAkI1gAAAIAJCNYAAACACQjWAAAAgAkI1gAAAIAJCNYAAACACQjWAAAAgAkI1gAAAIAJCNYAAACACewLugDgfvTwqLWyc3It6DKAAnEypl1BlwAAdwQj1gAAAIAJCNYAAACACQjWAAAAgAkI1gAAAIAJCNYAAACACQjW94mTJ0/KYrFoz549d/Q4cXFxslgsunz58h09DgAAwL2GYF1EREREyGKxWJcSJUooLCxM+/btK9C6soJ21lKmTBk9/fTTOnHiRIHWBQAAYDaCdRESFhamhIQEJSQkaMOGDbK3t1f79u0LuixJ0pEjR3TmzBl99tlnOnDggDp06KCMjIxs7QzD0PXr1wugwtzdizUBAIB7D8G6CHFycpKvr698fX31yCOP6I033tDp06d1/vz5HNt///33qlevnpycnOTn56c33njDJkCmpaVp4MCBKl26tJydndWoUSPt2LHDpo9Vq1apSpUqcnFxUfPmzXXy5Mkcj1W6dGn5+fmpSZMmGjlypA4ePKhjx45ZR7RXr16t2rVry8nJSZs2bVJmZqaio6NVsWJFubi4qGbNmvr888+t/V26dEndunVTqVKl5OLioqCgIM2fP1+SdO3aNQ0YMEB+fn5ydnZWQECAoqOjJeU8Jeby5cuyWCyKi4uTpHzXBAAA7m9882IRlZycrI8//liBgYEqUaKErl69arP9t99+U9u2bRUREaGFCxfq8OHD6tOnj5ydnTV69GhJ0vDhw7Vs2TItWLBAAQEBmjBhgkJDQ3Xs2DH5+Pjo9OnTeuqpp9S/f3/17dtXO3fu1JAhQ/62NhcXF0k3AnCWN954QxMnTlSlSpVUvHhxRUdH6+OPP9asWbMUFBSkjRs36oUXXlCpUqXUtGlTjRgxQgcPHtTq1atVsmRJHTt2TH/++ackaerUqVqxYoWWLl2q8uXL6/Tp0zp9+nSe38O81gQAAO5vBOsiZOXKlXJ3d5ckXb16VX5+flq5cqXs7LL/YmLGjBny9/fX9OnTZbFYVK1aNZ05c0avv/66Ro4cqT///FMzZ85UbGys2rRpI0maM2eO1q1bp3nz5mnYsGGaOXOmKleurEmTJkmSqlatqv379+udd97JtcaEhARNnDhR5cqVU9WqVbVlyxZJ0pgxY9SqVStJN0bKx48fr/Xr16tBgwaSpEqVKmnTpk2aPXu2mjZtqlOnTqlWrVqqU6eOJKlChQrWY5w6dUpBQUFq1KiRLBaLAgIC8vV+5rWmnKSlpSktLc36OikpKV+1AACAex/Bughp3ry5Zs6cKenGVIkZM2aoTZs22r59e7a2hw4dUoMGDWSxWKzrGjZsqOTkZP3666+6fPmy0tPT1bBhQ+t2BwcH1atXT4cOHbL2Ub9+fZt+s0LnzR544AEZhqGUlBTVrFlTy5Ytk6Ojo3V7VkCWpGPHjiklJcUaarNcu3ZNtWrVkiT169dPTz/9tHbt2qXWrVurU6dOeuyxxyTduJGzVatWqlq1qsLCwtS+fXu1bt3679/Am+S1ppxER0crKioqz8cGAACFD8G6CHFzc1NgYKD19dy5c+Xl5aU5c+aod+/eBViZ9MMPP8jT01OlS5eWh4dHtu1ubm7Wn5OTkyVJX3/9tcqVK2fTzsnJSZLUpk0b/fLLL1q1apXWrVunFi1aqH///po4caJCQkIUHx+v1atXa/369ercubNatmypzz//3Dp6bxiGtc/09PQca85rTTmJjIzU4MGDra+TkpLk7++fa3sAAFB4EayLMIvFIjs7O+vc47+qXr26li1bJsMwrKPWmzdvloeHhx544AGVKFFCjo6O2rx5s3UqRXp6unbs2KFBgwZZ+1ixYoVNvz/++GOOtVSsWFHe3t63VfeDDz4oJycnnTp16pZzl0uVKqXw8HCFh4ercePGGjZsmCZOnChJ8vT0VJcuXdSlSxc988wzCgsL08WLF1WqVClJN6akZI00386zvW+3pps5OTndMngDAICig2BdhKSlpens2bOSbkwFmT59upKTk9WhQ4dsbV9++WVNmTJFr7zyigYMGKAjR45o1KhRGjx4sOzs7OTm5qZ+/fpp2LBh8vHxUfny5TVhwgSlpKSoV69ekqSXXnpJkyZN0rBhw9S7d2/99NNPio2N/cfn4eHhoaFDh+q1115TZmamGjVqpMTERG3evFmenp4KDw/XyJEjVbt2bT300ENKS0vTypUrVb16dUnSe++9Jz8/P9WqVUt2dnb67LPP5OvrK29vb9nZ2enRRx9VTEyMKlasqHPnzunf//63KTUBAID7G8G6CFmzZo38/Pwk3QiC1apV02effaZmzZplewxeuXLltGrVKg0bNkw1a9aUj4+PevXqZRMyY2JilJmZqe7du+vKlSuqU6eO1q5dq+LFi0uSypcvr2XLlum1117TtGnTVK9ePY0fP149e/b8x+cyduxYlSpVStHR0Tpx4oS8vb0VEhKiN998U5Lk6OioyMhInTx5Ui4uLmrcuLEWL15sPfcJEybo6NGjKlasmOrWratVq1ZZp4F8+OGH6tWrl2rXrq2qVatqwoQJtzUH++9qAgAA9zeL8dfJpgDuqKSkJHl5ecl/0FLZObkWdDlAgTgZ066gSwCAPMn6/E5MTJSnp2eu7fiCGAAAAMAEBGsAAADABARrAAAAwAQEawAAAMAEBGsAAADABDxuDygAP0eF3vKuYgAAUPgwYg0AAACYgGANAAAAmIBgDQAAAJiAYA0AAACYgGANAAAAmIBgDQAAAJiAYA0AAACYgGANAAAAmIBgDQAAAJiAYA0AAACYgGANAAAAmIBgDQAAAJiAYA0AAACYgGANAAAAmIBgDQAAAJiAYA0AAACYgGANAAAAmIBgDQAAAJiAYA0AAACYgGANAAAAmIBgDQAAAJiAYA0AAACYwL6gCwDuRw+PWis7J9eCLgMoMk7GtCvoEgCAEWsAAADADARrAAAAwAQEawAAAMAEBGsAAADABARrAAAAwAQEawAAAMAEBGvc1ywWi5YvX17QZQAAgCKAYH2fi4iIkMVikcVikaOjowIDAzVmzBhdv369oEu7KxISEtSmTZuCLgMAABQBfEEMFBYWpvnz5ystLU2rVq1S//795eDgoMjISJt2165dk6OjYwFVeWf4+voWdAkAAKCIYMQacnJykq+vrwICAtSvXz+1bNlSK1asUEREhDp16qRx48apbNmyqlq1qiTp9OnT6ty5s7y9veXj46OOHTvq5MmT1v6uX7+ugQMHytvbWyVKlNDrr7+u8PBwderUydqmWbNmGjhwoIYPHy4fHx/5+vpq9OjRNnW99957qlGjhtzc3OTv76+XX35ZycnJ1u2xsbHy9vbW2rVrVb16dbm7uyssLEwJCQk2/Xz44Yd66KGH5OTkJD8/Pw0YMMC67eapIH93bnFxcapXr57c3Nzk7e2thg0b6pdffsn/mw8AAIoMgjWycXFx0bVr1yRJGzZs0JEjR7Ru3TqtXLlS6enpCg0NlYeHh3744Qdt3rzZGmiz9nnnnXe0aNEizZ8/X5s3b1ZSUlKO85gXLFggNzc3bdu2TRMmTNCYMWO0bt0663Y7OztNnTpVBw4c0IIFC/Ttt99q+PDhNn2kpKRo4sSJ+uijj7Rx40adOnVKQ4cOtW6fOXOm+vfvr759+2r//v1asWKFAgMDczzvvzu369evq1OnTmratKn27dunrVu3qm/fvrJYLLm+l2lpaUpKSrJZAABA0ZTvqSAfffSRZs2apfj4eG3dulUBAQGaMmWKKlasqI4dO5pZI+4SwzC0YcMGrV27Vq+88orOnz8vNzc3zZ071zoF5OOPP1ZmZqbmzp1rDZTz58+Xt7e34uLi1Lp1a02bNk2RkZF68sknJUnTp0/XqlWrsh0vODhYo0aNkiQFBQVp+vTp2rBhg1q1aiVJGjRokLVthQoV9Pbbb+ull17SjBkzrOvT09M1a9YsVa5cWZI0YMAAjRkzxrr97bff1pAhQ/Tqq69a19WtWzfH81+yZMktz61OnTpKTExU+/btrcerXr36Ld/T6OhoRUVF3bINAAAoGvI1Yj1z5kwNHjxYbdu21eXLl5WRkSFJ8vb21pQpU8ysD3fBypUr5e7uLmdnZ7Vp00ZdunSxTsuoUaOGzbzqvXv36tixY/Lw8JC7u7vc3d3l4+Oj1NRUHT9+XImJifr9999Vr1496z7FihVT7dq1sx03ODjY5rWfn5/OnTtnfb1+/Xq1aNFC5cqVk4eHh7p3764LFy4oJSXF2sbV1dUacm/u49y5czpz5oxatGhxW+/D352bj4+PIiIiFBoaqg4dOuj999/PNu3kZpGRkUpMTLQup0+fvq1aAABA4ZOvEetp06Zpzpw56tSpk2JiYqzr69SpY/NreBQOzZs318yZM+Xo6KiyZcvK3v7//li4ubnZtE1OTlbt2rW1aNGibP2UKlUqT8d1cHCweW2xWJSZmSlJOnnypNq3b69+/fpp3Lhx8vHx0aZNm9SrVy9du3ZNrq6uufZhGIakG1Na8uJ2zm3+/PkaOHCg1qxZoyVLlujf//631q1bp0cffTTHPp2cnOTk5JSnOgAAQOGUr2AdHx+vWrVqZVvv5OSkq1ev/uOicHe5ubnlOu/4ZiEhIVqyZIlKly4tT0/PHNuUKVNGO3bsUJMmTSRJGRkZ2rVrlx555JHbrumnn35SZmamJk2aJDu7G79YWbp06W3vL0keHh6qUKGCNmzYoObNm/9t+9s5N0mqVauWatWqpcjISDVo0ECffPJJrsEaAADcP/I1FaRixYras2dPtvVr1qz52zmnKNy6deumkiVLqmPHjvrhhx8UHx+vuLg4DRw4UL/++qsk6ZVXXlF0dLS+/PJLHTlyRK+++qouXbp0y5v8bhYYGKj09HRNmzZNJ06csM7pz6vRo0dr0qRJmjp1qo4ePapdu3Zp2rRp+Tq3+Ph4RUZGauvWrfrll1/0zTff6OjRo/yZBwAAkvI5Yj148GD1799fqampMgxD27dv16effqro6GjNnTvX7BpxD3F1ddXGjRv1+uuv66mnntKVK1dUrlw5tWjRwjrK+/rrr+vs2bN68cUXVaxYMfXt21ehoaEqVqzYbR+nZs2aeu+99/TOO+8oMjJSTZo0UXR0tF588cU81RseHq7U1FRNnjxZQ4cOVcmSJfXMM8/k69z+/PNPHT58WAsWLNCFCxfk5+en/v3761//+leeagIAAEWTxciakJpHixYt0ujRo3X8+HFJUtmyZRUVFaVevXqZWiAKv8zMTFWvXl2dO3fW2LFjC7qcApWUlCQvLy/5D1oqOyfXgi4HKDJOxrQr6BIAFGFZn9+JiYm3nC6a5xHr69ev65NPPlFoaKi6deumlJQUJScnq3Tp0v+oYBQdWdMkmjZtqrS0NE2fPl3x8fF6/vnnC7o0AACAOybPc6zt7e310ksvKTU1VdKNX58TqvFXdnZ2io2NVd26ddWwYUPt379f69evZy4yAAAo0vI1x7pevXravXu3AgICzK4HRYC/v782b95c0GUAAADcVfkK1i+//LKGDBmiX3/9VbVr1872rOObv/gDAAAAKOrydfNi1nOFbTr6/1/MYbFYrN/ECMDW7d78AAAA7h137OZF6cYXxAAAAAD4P/kK1sytBgAAAGzlK1gvXLjwltvz+iUeAAAAQGGXrznWxYsXt3mdnp6ulJQUOTo6ytXVVRcvXjStQKAoYY41AACFz+1+fuf5OdaSdOnSJZslOTlZR44cUaNGjfTpp5/mu2gAAACgsMpXsM5JUFCQYmJi9Oqrr5rVJQAAAFBomBaspRvfynjmzBkzuwQAAAAKhXzdvLhixQqb14ZhKCEhQdOnT1fDhg1NKQwAAAAoTPIVrDt16mTz2mKxqFSpUnr88cc1adIkM+oCAAAACpV8BevMzEyz6wAAAAAKtXzNsR4zZoxSUlKyrf/zzz81ZsyYf1wUAAAAUNjk6znWxYoVU0JCgkqXLm2z/sKFCypdurQyMjJMKxAoSniONQAAhc8dfY61YRiyWCzZ1u/du1c+Pj756RIAAAAo1PI0x7p48eKyWCyyWCyqUqWKTbjOyMhQcnKyXnrpJdOLBAAAAO51eQrWU6ZMkWEY6tmzp6KiouTl5WXd5ujoqAoVKqhBgwamFwkAAADc6/IUrMPDwyVJFStW1GOPPSYHB4c7UhQAAABQ2OTrcXtNmza1/pyamqpr167ZbOemLAAAANxv8nXzYkpKigYMGKDSpUvLzc1NxYsXt1kAAACA+02+gvWwYcP07bffaubMmXJyctLcuXMVFRWlsmXLauHChWbXCAAAANzz8jUV5KuvvtLChQvVrFkz9ejRQ40bN1ZgYKACAgK0aNEidevWzew6AQAAgHtavkasL168qEqVKkm6MZ/64sWLkqRGjRpp48aN5lUHAAAAFBL5CtaVKlVSfHy8JKlatWpaunSppBsj2d7e3qYVBwAAABQW+ZoK0qNHD+3du1dNmzbVG2+8oQ4dOmj69OlKT0/Xe++9Z3aNQJHz8Ki1snNyLegyAOTiZEy7gi4BQCGUr2D92muvWX9u2bKlDh8+rJ9++kmBgYEKDg42rTgAAACgsMhXsP6r1NRUBQQEKCAgwIx6AAAAgEIpX3OsMzIyNHbsWJUrV07u7u46ceKEJGnEiBGaN2+eqQUCAAAAhUG+gvW4ceMUGxurCRMmyNHR0br+4Ycf1ty5c00rDgAAACgs8hWsFy5cqA8++EDdunVTsWLFrOtr1qypw4cPm1YcAAAAUFjkK1j/9ttvCgwMzLY+MzNT6enp/7goAAAAoLDJV7B+8MEH9cMPP2Rb//nnn6tWrVr/uKibWSwWLV++/Lbbjx49Wo888ojpddyrIiIi1KlTJ+vrZs2aadCgQQVWT2Fw83sGAADwT+UrWI8cOVIDBgzQO++8o8zMTH3xxRfq06ePxo0bp5EjR952PxEREbJYLLJYLHJwcFCZMmXUqlUrffjhh8rMzLS2S0hIUJs2bfJTar6dPHlSFotFe/bsMbXfChUqWM/Zzc1NISEh+uyzz0w9xhdffKGxY8ea2md+xcbGWs/3r8vdmouf23V8//33FRsbe1dqAAAA94c8BesTJ07IMAx17NhRX331ldavXy83NzeNHDlShw4d0ldffaVWrVrlqYCwsDAlJCTo5MmTWr16tZo3b65XX31V7du31/Xr1yVJvr6+cnJyylO/97IxY8YoISFBu3fvVt26ddWlSxdt2bLFtP59fHzk4eHxj/owc0qPp6enEhISbJZu3bqZ1n9+eHl58S2hAADAVHkK1kFBQTp//rwkqXHjxvLx8dH+/fuVkpKiTZs2qXXr1nkuwMnJSb6+vipXrpxCQkL05ptv6ssvv9Tq1autI4o3TwV5/fXXVaVKFbm6uqpSpUoaMWJEjkFw9uzZ8vf3l6urqzp37qzExESb7XPnzlX16tXl7OysatWqacaMGdZtFStWlCTVqlVLFotFzZo1u639rl27pgEDBsjPz0/Ozs4KCAhQdHS0zXE9PDzk6+urKlWq6D//+Y9cXFz01VdfSZJOnz6tzp07y9vbWz4+PurYsaNOnjxp3TcjI0ODBw+Wt7e3SpQooeHDh8swDJv+b54KkpCQoHbt2snFxUUVK1bUJ598ogoVKmjKlCnWNhaLRTNnztQTTzwhNzc3jRs3TpL05ZdfKiQkRM7OzqpUqZKioqKs/+CRpMuXL6t3794qVaqUPD099fjjj2vv3r029VgsFvn6+tosLi4uio2NzRZuly9fLovFYn2dNa3no48+UoUKFeTl5aWuXbvqypUr1jaZmZmaMGGCAgMD5eTkpPLly1vrz+063jwVJC0tTQMHDlTp0qXl7OysRo0aaceOHdbtcXFxslgs2rBhg+rUqSNXV1c99thjOnLkiAAAAKQ8BuubA9zq1at19epVUwuSpMcff1w1a9bUF198keN2Dw8PxcbG6uDBg3r//fc1Z84cTZ482abNsWPHtHTpUn311Vdas2aNdu/erZdfftm6fdGiRRo5cqTGjRunQ4cOafz48RoxYoQWLFggSdq+fbskaf369UpISLDW8nf7TZ06VStWrNDSpUt15MgRLVq0SBUqVMj1XO3t7eXg4KBr164pPT1doaGh8vDw0A8//KDNmzfL3d1dYWFhunbtmiRp0qRJio2N1YcffqhNmzbp4sWL+u9//3vL9/PFF1/UmTNnFBcXp2XLlumDDz7QuXPnsrUbPXq0nnzySe3fv189e/bUDz/8oBdffFGvvvqqDh48qNmzZys2NtYaWiXp2Wef1blz57R69Wr99NNPCgkJUYsWLXTx4sVb1pQXx48f1/Lly7Vy5UqtXLlS33//vWJiYqzbIyMjFRMToxEjRujgwYP65JNPVKZMGUm5X8ebDR8+XMuWLdOCBQu0a9cuBQYGKjQ0NNt5vPXWW5o0aZJ27twpe3t79ezZ85a1p6WlKSkpyWYBAABF0z/65sWbg7aZqlWrpn379uW47d///rf15woVKmjo0KFavHixhg8fbl2fmpqqhQsXqly5cpKkadOmqV27dpo0aZJ8fX01atQoTZo0SU899ZSkGyObWeExPDxcpUqVkiSVKFFCvr6+1n7/br9Tp04pKChIjRo1ksViueU3Ul67dk2TJk1SYmKiHn/8cS1ZskSZmZmaO3euddR2/vz58vb2VlxcnFq3bq0pU6YoMjLSevxZs2Zp7dq1uR7j8OHDWr9+vXbs2KE6depIujHiHhQUlK3t888/rx49elhf9+zZU2+88YbCw8MlSZUqVdLYsWM1fPhwjRo1Sps2bdL27dt17tw561SdiRMnavny5fr888/Vt29fSVJiYqLc3d2t/bq7u+vs2bO51nyzzMxMxcbGWqe3dO/eXRs2bNC4ceN05coVvf/++5o+fbq1zsqVK6tRo0aSlOt1/KurV69q5syZio2Ntc7lnzNnjtatW6d58+Zp2LBh1rbjxo1T06ZNJUlvvPGG2rVrp9TUVDk7O+fYd3R0tKKiom77XAEAQOGVp2CddePZzevuBMMwcu17yZIlmjp1qo4fP67k5GRdv35dnp6eNm3Kly9vDdWS1KBBA2VmZurIkSPy8PDQ8ePH1atXL/Xp08fa5vr16/Ly8sq1pqtXr/7tfhEREWrVqpWqVq2qsLAwtW/fPtsUmddff13//ve/lZqaKnd3d8XExKhdu3YaNmyYjh07lm1+dGpqqo4fP67ExEQlJCSofv361m329vaqU6dOrv/IOXLkiOzt7RUSEmJdFxgYqOLFi2drmxW8s+zdu1ebN2+2GaHOyMhQamqqUlJStHfvXiUnJ6tEiRI2+/355586fvy49bWHh4d27dplfW1nl7d7ZitUqGDznvj5+VlH3A8dOqS0tDS1aNEiT33+1fHjx5Wenq6GDRta1zk4OKhevXo6dOiQTdvg4GCbOiTp3LlzKl++fI59R0ZGavDgwdbXSUlJ8vf3z3etAADg3pWnYG0YhiIiIqyjk6mpqXrppZfk5uZm0y63X7fnxaFDh6zzY/9q69at6tatm6KiohQaGiovLy8tXrxYkyZNuu2+k5OTJd0YlfxrSJVk84U3+dkvJCRE8fHxWr16tdavX6/OnTurZcuW+vzzz61thw0bpoiICLm7u6tMmTLWf0AkJyerdu3aWrRoUbZjZ4283kk3X8fk5GRFRUVZR8f/ytnZWcnJyfLz81NcXFy27X+dO21nZ5fjc8/t7Oyy/YMgp7nyDg4ONq8tFov1qTEuLi65ns+d8Ndasq7bX59gczMnJ6cideMtAADIXZ6Cddav2rO88MILphaT5dtvv9X+/fv12muvZdu2ZcsWBQQE6K233rKu++WXX7K1O3XqlM6cOaOyZctKkn788UfZ2dmpatWqKlOmjMqWLasTJ07k+nSKrK9qz8jIsK67nf2kG0/B6NKli7p06aJnnnlGYWFhunjxonx8fCRJJUuWzDFohoSEaMmSJSpdunS2Efgsfn5+2rZtm5o0aSLpxmh51tzmnFStWlXXr1/X7t27Vbt2bUk35p9funQp1/r/Ws+RI0dyrDVr+9mzZ2Vvb3/LeeS5KVWqlK5cuaKrV69aQ31eH28YFBQkFxcXbdiwQb179862PafreLPKlSvL0dFRmzdvtk7dSU9P144dO3geOAAAuG15Ctbz5883vYC0tDSdPXtWGRkZ+v3337VmzRpFR0erffv2evHFF7O1DwoK0qlTp7R48WLVrVtXX3/9dY437zk7Oys8PFwTJ05UUlKSBg4cqM6dO1vn2UZFRWngwIHy8vJSWFiY0tLStHPnTl26dEmDBw9W6dKl5eLiojVr1uiBBx6Qs7OzvLy8/na/9957T35+fqpVq5bs7Oz02WefydfX97Ye7datWze9++676tixo8aMGaMHHnhAv/zyi7744gsNHz5cDzzwgF599VXFxMQoKChI1apV03vvvafLly/n2me1atXUsmVL9e3bVzNnzpSDg4OGDBkiFxeXv53GM3LkSLVv317ly5fXM888Izs7O+3du1c///yz3n77bbVs2VINGjRQp06dNGHCBFWpUkVnzpzR119/rSeffDLb1JKb1a9fX66urnrzzTc1cOBAbdu2Lc/PlnZ2dtbrr7+u4cOHy9HRUQ0bNtT58+d14MAB9erVK9fr+Fdubm7q16+fhg0bJh8fH5UvX14TJkxQSkqKevXqlad6AADA/StfXxBjpjVr1sjPz08VKlRQWFiYvvvuO02dOlVffvlljtMynnjiCb322msaMGCAHnnkEW3ZskUjRozI1i4wMFBPPfWU2rZtq9atWys4ONjmsXi9e/fW3LlzNX/+fNWoUUNNmzZVbGysdfqJvb29pk6dqtmzZ6ts2bLq2LHjbe3n4eGhCRMmqE6dOqpbt65OnjypVatW3da8YldXV23cuFHly5fXU089perVq6tXr15KTU21jmAPGTJE3bt3V3h4uBo0aCAPDw89+eSTt+x34cKFKlOmjJo0aaInn3xSffr0kYeHR6433GUJDQ3VypUr9c0336hu3bp69NFHNXnyZOuorsVi0apVq9SkSRP16NFDVapUUdeuXfXLL79Yn8pxKz4+Pvr444+1atUq1ahRQ59++qlGjx79t/vdbMSIERoyZIhGjhyp6tWrq0uXLtY52Lldx5vFxMTo6aefVvfu3RUSEqJjx45p7dq1Oc5FBwAAyInFuJOP9sA96ddff5W/v7/Wr1//j276Q94lJSXJy8tL/oOWys7JtaDLAZCLkzHtCroEAPeQrM/vxMTEXKfrSv/wcXsoHL799lslJyerRo0aSkhI0PDhw1WhQgXrPG0AAAD8cwTr+0B6errefPNNnThxQh4eHnrssce0aNGibE/bAAAAQP4RrO8DoaGhCg0NLegyAAAAirQCv3kRAAAAKAoI1gAAAIAJmAoCFICfo0JveVcxAAAofBixBgAAAExAsAYAAABMQLAGAAAATECwBgAAAExAsAYAAABMQLAGAAAATECwBgAAAExAsAYAAABMQLAGAAAATECwBgAAAExAsAYAAABMQLAGAAAATECwBgAAAExAsAYAAABMQLAGAAAATECwBgAAAExAsAYAAABMQLAGAAAATECwBgAAAExAsAYAAABMQLAGAAAATECwBgAAAExgX9AFAPejh0etlZ2Ta0GXAaAIOBnTrqBLAPD/MWINAAAAmIBgDQAAAJiAYA0AAACYgGANAAAAmIBgDQAAAJiAYA0AAACYgGCN+0ZERIQsFot1KVGihMLCwrRv3z5rm6xtP/74o82+aWlpKlGihCwWi+Li4mzaL1++/C6dAQAAuJcRrHFfCQsLU0JCghISErRhwwbZ29urffv2Nm38/f01f/58m3X//e9/5e7ufjdLBQAAhQzBGvcVJycn+fr6ytfXV4888ojeeOMNnT59WufPn7e2CQ8P1+LFi/Xnn39a13344YcKDw8viJIBAEAhQbDGfSs5OVkff/yxAgMDVaJECev62rVrq0KFClq2bJkk6dSpU9q4caO6d++e52OkpaUpKSnJZgEAAEUTwRr3lZUrV8rd3V3u7u7y8PDQihUrtGTJEtnZ2f5V6Nmzpz788ENJUmxsrNq2batSpUrl+XjR0dHy8vKyLv7+/qacBwAAuPcQrHFfad68ufbs2aM9e/Zo+/btCg0NVZs2bfTLL7/YtHvhhRe0detWnThxQrGxserZs2e+jhcZGanExETrcvr0aTNOAwAA3IMI1rivuLm5KTAwUIGBgapbt67mzp2rq1evas6cOTbtSpQoofbt26tXr15KTU1VmzZt8nU8JycneXp62iwAAKBoIljjvmaxWGRnZ2dzo2KWnj17Ki4uTi+++KKKFStWANUBAIDCxL6gCwDuprS0NJ09e1aSdOnSJU2fPl3Jycnq0KFDtrZhYWE6f/48o8wAAOC2EKxxX1mzZo38/PwkSR4eHqpWrZo+++wzNWvWLFtbi8WikiVL3uUKAQBAYUWwxn0jNjZWsbGxt2xjGEau27y9vbNtv1V7AABwf2GONQAAAGACgjUAAABgAoI1AAAAYAKCNQAAAGACgjUAAABgAp4KAhSAn6NCeT42AABFDCPWAAAAgAkI1gAAAIAJCNYAAACACQjWAAAAgAkI1gAAAIAJCNYAAACACQjWAAAAgAkI1gAAAIAJCNYAAACACQjWAAAAgAkI1gAAAIAJCNYAAACACQjWAAAAgAkI1gAAAIAJCNYAAACACQjWAAAAgAkI1gAAAIAJCNYAAACACQjWAAAAgAkI1gAAAIAJCNYAAACACQjWAAAAgAnsC7oA4H708Ki1snNyLegyABRRJ2PaFXQJwH2JEWsAAADABARrAAAAwAQEawAAAMAEBGsAAADABARrAAAAwAQEawAAAMAEBGvcN7Zu3apixYqpXTseQwUAAMxHsMZ9Y968eXrllVe0ceNGnTlzpqDLAQAARQzBGveF5ORkLVmyRP369VO7du0UGxtrs33FihUKCgqSs7OzmjdvrgULFshisejy5cvWNps2bVLjxo3l4uIif39/DRw4UFevXr27JwIAAO5ZBGvcF5YuXapq1aqpatWqeuGFF/Thhx/KMAxJUnx8vJ555hl16tRJe/fu1b/+9S+99dZbNvsfP35cYWFhevrpp7Vv3z4tWbJEmzZt0oABA2553LS0NCUlJdksAACgaCJY474wb948vfDCC5KksLAwJSYm6vvvv5ckzZ49W1WrVtW7776rqlWrqmvXroqIiLDZPzo6Wt26ddOgQYMUFBSkxx57TFOnTtXChQuVmpqa63Gjo6Pl5eVlXfz9/e/YOQIAgIJFsEaRd+TIEW3fvl3PPfecJMne3l5dunTRvHnzrNvr1q1rs0+9evVsXu/du1exsbFyd3e3LqGhocrMzFR8fHyux46MjFRiYqJ1OX36tMlnBwAA7hX2BV0AcKfNmzdP169fV9myZa3rDMOQk5OTpk+fflt9JCcn61//+pcGDhyYbVv58uVz3c/JyUlOTk55LxoAABQ6BGsUadevX9fChQs1adIktW7d2mZbp06d9Omnn6pq1apatWqVzbYdO3bYvA4JCdHBgwcVGBh4x2sGAACFE8EaRdrKlSt16dIl9erVS15eXjbbnn76ac2bN09Lly7Ve++9p9dff129evXSnj17rE8NsVgskqTXX39djz76qAYMGKDevXvLzc1NBw8e1Lp162571BsAABRtzLFGkTZv3jy1bNkyW6iWbgTrnTt36sqVK/r888/1xRdfKDg4WDNnzrQ+FSRrGkdwcLC+//57/e9//1Pjxo1Vq1YtjRw50mZ6CQAAuL9ZjKxnjgGwGjdunGbNmmX6zYZJSUk3ng4yaKnsnFxN7RsAspyM4RtmATNlfX4nJibK09Mz13ZMBQEkzZgxQ3Xr1lWJEiW0efNmvfvuu3/7jGoAAIC/IlgDko4ePaq3335bFy9eVPny5TVkyBBFRkYWdFkAAKAQIVgDkiZPnqzJkycXdBkAAKAQ4+ZFAAAAwAQEawAAAMAETAUBCsDPUaG3vKsYAAAUPoxYAwAAACYgWAMAAAAmIFgDAAAAJiBYAwAAACYgWAMAAAAmIFgDAAAAJiBYAwAAACYgWAMAAAAmIFgDAAAAJiBYAwAAACYgWAMAAAAmIFgDAAAAJiBYAwAAACYgWAMAAAAmIFgDAAAAJiBYAwAAACYgWAMAAAAmIFgDAAAAJiBYAwAAACYgWAMAAAAmIFgDAAAAJiBYAwAAACawL+gCgPvRw6PWys7JtaDLAACgyDgZ066gS2DEGgAAADADwRoAAAAwAcEaAAAAMAHBGgAAADABwRoAAAAwAcEa9yyLxaLly5cXdBkAAAC3hWCNAnH27Fm9+uqrCgwMlLOzs8qUKaOGDRtq5syZSklJKejyAAAA8oznWOOuO3HihBo2bChvb2+NHz9eNWrUkJOTk/bv368PPvhA5cqV0xNPPFHQZQIAAOQJI9a4615++WXZ29tr586d6ty5s6pXr65KlSqpY8eO+vrrr9WhQ4ds+8TFxclisejy5cvWdXv27JHFYtHJkyet6zZv3qxmzZrJ1dVVxYsXV2hoqC5duiRJSktL08CBA1W6dGk5OzurUaNG2rFjh3XfS5cuqVu3bipVqpRcXFwUFBSk+fPnW7efPn1anTt3lre3t3x8fNSxY0ebYwMAgPsbwRp31YULF/TNN9+of//+cnNzy7GNxWLJV9979uxRixYt9OCDD2rr1q3atGmTOnTooIyMDEnS8OHDtWzZMi1YsEC7du1SYGCgQkNDdfHiRUnSiBEjdPDgQa1evVqHDh3SzJkzVbJkSUlSenq6QkND5eHhoR9++EGbN2+Wu7u7wsLCdO3atXzVCwAAihamguCuOnbsmAzDUNWqVW3WlyxZUqmpqZKk/v3765133slz3xMmTFCdOnU0Y8YM67qHHnpIknT16lXNnDlTsbGxatOmjSRpzpw5WrdunebNm6dhw4bp1KlTqlWrlurUqSNJqlChgrWfJUuWKDMzU3PnzrUG//nz58vb21txcXFq3bp1jjWlpaUpLS3N+jopKSnP5wUAAAoHRqxxT9i+fbv27Nmjhx56yCaI5kXWiHVOjh8/rvT0dDVs2NC6zsHBQfXq1dOhQ4ckSf369dPixYv1yCOPaPjw4dqyZYu17d69e3Xs2DF5eHjI3d1d7u7u8vHxUWpqqo4fP55rTdHR0fLy8rIu/v7++To3AABw72PEGndVYGCgLBaLjhw5YrO+UqVKkiQXF5cc97Ozu/FvQMMwrOvS09Nt2uS27+1q06aNfvnlF61atUrr1q1TixYt1L9/f02cOFHJycmqXbu2Fi1alG2/UqVK5dpnZGSkBg8ebH2dlJREuAYAoIhixBp3VYkSJdSqVStNnz5dV69eve39ssJrQkKCdd2ePXts2gQHB2vDhg057l+5cmU5Ojpq8+bN1nXp6enasWOHHnzwQZvjhIeH6+OPP9aUKVP0wQcfSJJCQkJ09OhRlS5dWoGBgTaLl5dXrnU7OTnJ09PTZgEAAEUTwRp33YwZM3T9+nXVqVNHS5Ys0aFDh3TkyBF9/PHHOnz4sIoVK5Ztn8DAQPn7+2v06NE6evSovv76a02aNMmmTWRkpHbs2KGXX35Z+/bt0+HDhzVz5kz98ccfcnNzU79+/TRs2DCtWbNGBw8eVJ8+fZSSkqJevXpJkkaOHKkvv/xSx44d04EDB7Ry5UpVr15dktStWzeVLFlSHTt21A8//KD4+HjFxcVp4MCB+vXXX+/8mwYAAO55BGvcdZUrV9bu3bvVsmVLRUZGqmbNmqpTp46mTZumoUOHauzYsdn2cXBw0KeffqrDhw8rODhY77zzjt5++22bNlWqVNE333yjvXv3ql69emrQoIG+/PJL2dvfmPEUExOjp59+Wt27d1dISIiOHTumtWvXqnjx4pIkR0dHRUZGKjg4WE2aNFGxYsW0ePFiSZKrq6s2btyo8uXL66mnnlL16tXVq1cvpaamMgoNAAAkSRbjr5NWAdxRSUlJN25iHLRUdk6uBV0OAABFxsmYdnes76zP78TExFsOqDFiDQAAAJiAYA0AAACYgGANAAAAmIBgDQAAAJiAYA0AAACYgG9eBArAz1GhPKYPAIAihhFrAAAAwAQEawAAAMAEBGsAAADABARrAAAAwAQEawAAAMAEBGsAAADABARrAAAAwAQEawAAAMAEBGsAAADABARrAAAAwAR8pTlwFxmGIUlKSkoq4EoAAMDtyvrczvoczw3BGriLLly4IEny9/cv4EoAAEBeXblyRV5eXrluJ1gDd5GPj48k6dSpU7f8i4m7JykpSf7+/jp9+rQ8PT0LuhyIa3Iv4prce7gmd5dhGLpy5YrKli17y3YEa+AusrO7cVuDl5cX/yO8x3h6enJN7jFck3sP1+TewzW5e25nQIybFwEAAAATEKwBAAAAExCsgbvIyclJo0aNkpOTU0GXgv+Pa3Lv4Zrce7gm9x6uyb3JYvzdc0MAAAAA/C1GrAEAAAATEKwBAAAAExCsAQAAABMQrAEAAAATEKyBf+A///mPKlSoIGdnZ9WvX1/bt2+/ZfvPPvtM1apVk7Ozs2rUqKFVq1bZbDcMQyNHjpSfn59cXFzUsmVLHT169E6eQpFj9jWJiIiQxWKxWcLCwu7kKRQ5ebkmBw4c0NNPP60KFSrIYrFoypQp/7hPZGf2NRk9enS2vyfVqlW7g2dQ9OTlmsyZM0eNGzdW8eLFVbx4cbVs2TJbez5PCgbBGsinJUuWaPDgwRo1apR27dqlmjVrKjQ0VOfOncux/ZYtW/Tcc8+pV69e2r17tzp16qROnTrp559/traZMGGCpk6dqlmzZmnbtm1yc3NTaGioUlNT79ZpFWp34ppIUlhYmBISEqzLp59+ejdOp0jI6zVJSUlRpUqVFBMTI19fX1P6hK07cU0k6aGHHrL5e7Jp06Y7dQpFTl6vSVxcnJ577jl999132rp1q/z9/dW6dWv99ttv1jZ8nhQQA0C+1KtXz+jfv7/1dUZGhlG2bFkjOjo6x/adO3c22rVrZ7Oufv36xr/+9S/DMAwjMzPT8PX1Nd59913r9suXLxtOTk7Gp59+egfOoOgx+5oYhmGEh4cbHTt2vCP13g/yek3+KiAgwJg8ebKpfeLOXJNRo0YZNWvWNLHK+8s//TN9/fp1w8PDw1iwYIFhGHyeFCRGrIF8uHbtmn766Se1bNnSus7Ozk4tW7bU1q1bc9xn69atNu0lKTQ01No+Pj5eZ8+etWnj5eWl+vXr59on/s+duCZZ4uLiVLp0aVWtWlX9+vXThQsXzD+BIig/16Qg+ryf3Mn37+jRoypbtqwqVaqkbt266dSpU/+03PuCGdckJSVF6enp8vHxkcTnSUEiWAP58McffygjI0NlypSxWV+mTBmdPXs2x33Onj17y/ZZ/81Ln/g/d+KaSDemgSxcuFAbNmzQO++8o++//15t2rRRRkaG+SdRxOTnmhREn/eTO/X+1a9fX7GxsVqzZo1mzpyp+Ph4NW7cWFeuXPmnJRd5ZlyT119/XWXLlrUGaT5PCo59QRcAAPeyrl27Wn+uUaOGgoODVblyZcXFxalFixYFWBlw72jTpo315+DgYNWvX18BAQFaunSpevXqVYCVFX0xMTFavHix4uLi5OzsXNDl3PcYsQbyoWTJkipWrJh+//13m/W///57rjf3+Pr63rJ91n/z0if+z524JjmpVKmSSpYsqWPHjv3zoou4/FyTgujzfnK33j9vb29VqVKFvye34Z9ck4kTJyomJkbffPONgoODrev5PCk4BGsgHxwdHVW7dm1t2LDBui4zM1MbNmxQgwYNctynQYMGNu0lad26ddb2FStWlK+vr02bpKQkbdu2Ldc+8X/uxDXJya+//qoLFy7Iz8/PnMKLsPxck4Lo835yt96/5ORkHT9+nL8ntyG/12TChAkaO3as1qxZozp16ths4/OkABX03ZNAYbV48WLDycnJiI2NNQ4ePGj07dvX8Pb2Ns6ePWsYhmF0797deOONN6ztN2/ebNjb2xsTJ040Dh06ZIwaNcpwcHAw9u/fb20TExNjeHt7G19++aWxb98+o2PHjkbFihWNP//8866fX2Fk9jW5cuWKMXToUGPr1q1GfHy8sX79eiMkJMQICgoyUlNTC+QcC5u8XpO0tDRj9+7dxu7duw0/Pz9j6NChxu7du42jR4/edp+4tTtxTYYMGWLExcUZ8fHxxubNm42WLVsaJUuWNM6dO3fXz68wyus1iYmJMRwdHY3PP//cSEhIsC5XrlyxacPnyd1HsAb+gWnTphnly5c3HB0djXr16hk//vijdVvTpk2N8PBwm/ZLly41qlSpYjg6OhoPPfSQ8fXXX9tsz8zMNEaMGGGUKVPGcHJyMlq0aGEcOXLkbpxKkWHmNUlJSTFat25tlCpVynBwcDACAgKMPn36EODyKC/XJD4+3pCUbWnatOlt94m/Z/Y16dKli+Hn52c4Ojoa5cqVM7p06WIcO3bsLp5R4ZeXaxIQEJDjNRk1apS1DZ8nBcNiGIZRAAPlAAAAQJHCHGsAAADABARrAAAAwAQEawAAAMAEBGsAAADABARrAAAAwAQEawAAAMAEBGsAAADABARrAAAAwAQEawBAoRIREaFOnToVdBk5OnnypCwWi/bs2VPQpQAoAARrAABMcO3atYIuAUABI1gDAAqtZs2a6ZVXXtGgQYNUvHhxlSlTRnPmzNHVq1fVo0cPeXh4KDAwUKtXr7buExcXJ4vFoq+//lrBwcFydnbWo48+qp9//tmm72XLlumhhx6Sk5OTKlSooEmTJtlsr1ChgsaOHasXX3xRnp6e6tu3rypWrChJqlWrliwWi5o1ayZJ2rFjh1q1aqWSJUvKy8tLTZs21a5du2z6s1gsmjt3rp588km5uroqKChIK1assGlz4MABtW/fXp6envLw8FDjxo11/Phx6/a5c+eqevXqcnZ2VrVq1TRjxox//B4DuH0EawBAobZgwQKVLFlS27dv1yuvvKJ+/frp2Wef1WOPPaZdu3apdevW6t69u1JSUmz2GzZsmCZNmqQdO3aoVKlS6tChg9LT0yVJP/30kzp37qyuXbtq//79Gj16tEaMGKHY2FibPiZOnKiaNWtq9+7dGjFihLZv3y5JWr9+vRISEvTFF19Ikq5cuaLw8HBt2rRJP/74o4KCgtS2bVtduXLFpr+oqCh17txZ+/btU9u2bdWtWzddvHhRkvTbb7+pSZMmcnJy0rfffquffvpJPXv21PXr1yVJixYt0siRIzVu3DgdOnRI48eP14gRI7RgwQLT33MAuTAAAChEwsPDjY4dOxqGYRhNmzY1GjVqZN12/fp1w83Nzejevbt1XUJCgiHJ2Lp1q2EYhvHdd98ZkozFixdb21y4cMFwcXExlixZYhiGYTz//PNGq1atbI47bNgw48EHH7S+DggIMDp16mTTJj4+3pBk7N69+5bnkJGRYXh4eBhfffWVdZ0k49///rf1dXJysiHJWL16tWEYhhEZGWlUrFjRuHbtWo59Vq5c2fjkk09s1o0dO9Zo0KDBLWsBYB5GrAEAhVpwcLD152LFiqlEiRKqUaOGdV2ZMmUkSefOnbPZr0GDBtaffXx8VLVqVR06dEiSdOjQITVs2NCmfcOGDXX06FFlZGRY19WpU+e2avz999/Vp08fBQUFycvLS56enkpOTtapU6dyPRc3Nzd5enpa696zZ48aN24sBweHbP1fvXpVx48fV69eveTu7m5d3n77bZupIgDuLPuCLgAAgH/i5qBpsVhs1lksFklSZmam6cd2c3O7rXbh4eG6cOGC3n//fQUEBMjJyUkNGjTIdsNjTueSVbeLi0uu/ScnJ0uS5syZo/r169tsK1as2G3VCOCfI1gDAO5LP/74o8qXLy9JunTpkv73v/+pevXqkqTq1atr8+bNNu03b96sKlWq3DKoOjo6SpLNqHbWvjNmzFDbtm0lSadPn9Yff/yRp3qDg4O1YMECpaenZwvgZcqUUdmyZXXixAl169YtT/0CMA/BGgBwXxozZoxKlCihMmXK6K233lLJkiWtz8ceMmSI6tatq7Fjx6pLly7aunWrpk+f/rdP2ShdurRcXFy0Zs0aPfDAA3J2dpaXl5eCgoL00UcfqU6dOkpKStKwYcNuOQKdkwEDBmjatGnq2rWrIiMj5eXlpR9//FH16tVT1apVFRUVpYEDB8rLy0thYWFKS0vTzp07denSJQ0ePDi/bxOAPGCONQDgvhQTE6NXX31VtWvX1tmzZ/XVV19ZR5xDQkK0dOlSLV68WA8//LBGjhypMWPGKCIi4pZ92tvba+rUqZo9e7bKli2rjh07SpLmzZunS5cuKSQkRN27d9fAgQNVunTpPNVbokQJffvtt0pOTlbTpk1Vu3ZtzZkzxzp63bt3b82dO1fz589XjRo11LRpU8XGxlofAQjgzrMYhmEUdBEAANwtcXFxat68uS5duiRvb++CLgdAEcKINQAAAGACgjUAAABgAqaCAAAAACZgxBoAAAAwAcEaAAAAMAHBGgAAADABwRoAAAAwAcEaAAAAMAHBGgAAADABwRoAAAAwAcEaAAAAMAHBGgAAADDB/wM64LJeF7e6ZQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.feature_selection import SelectKBest, chi2\n",
    "\n",
    "# Split the dataset into X and y\n",
    "X = dataset.drop(columns=[\"Outcome\"])\n",
    "y = dataset[\"Outcome\"]\n",
    "\n",
    "# Feature selection with SelectKBest\n",
    "k = 8\n",
    "selector = SelectKBest(score_func=chi2, k=k)\n",
    "X_new = selector.fit_transform(X, y)\n",
    "\n",
    "# Get the selected feature names\n",
    "feature_names = X.columns[selector.get_support()]\n",
    "\n",
    "# Feature importance with ExtraTreesClassifier\n",
    "model = ExtraTreesClassifier()\n",
    "model.fit(X_new, y)\n",
    "\n",
    "# Plot the feature importances\n",
    "feat_importances = pd.Series(model.feature_importances_, index=feature_names)\n",
    "feat_importances.nlargest(k).plot(kind='barh')\n",
    "plt.title(\"Feature Importances\")\n",
    "plt.xlabel(\"Importance\")\n",
    "plt.ylabel(\"Feature\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "VqkiCi69gm_O",
   "metadata": {
    "id": "VqkiCi69gm_O"
   },
   "source": [
    "### List of columns sorted by Pearson correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "643af4d3",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1686345158419,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "643af4d3",
    "outputId": "28cfb9cb-426f-4536-b32e-217c70b89619"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Glucose                     0.472751\n",
       "BMI                         0.303764\n",
       "Age                         0.239968\n",
       "Insulin                     0.235812\n",
       "Pregnancies                 0.221692\n",
       "SkinThickness               0.215241\n",
       "DiabetesPedigreeFunction    0.181521\n",
       "BloodPressure               0.100662\n",
       "dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "pear_coor = (dataset.drop('Outcome', axis=1).corrwith(dataset.Outcome).abs().sort_values(ascending=False))\n",
    "pear_coor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3af8175c",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 8,
     "status": "ok",
     "timestamp": 1686345158419,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "3af8175c",
    "outputId": "66b4647f-4202-49b1-cc1d-105147d60136"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Glucose                     0.472751\n",
       "BMI                         0.303764\n",
       "Age                         0.239968\n",
       "Insulin                     0.235812\n",
       "Pregnancies                 0.221692\n",
       "SkinThickness               0.215241\n",
       "DiabetesPedigreeFunction    0.181521\n",
       "BloodPressure               0.100662\n",
       "dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pear_coor[pear_coor>0.05]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Iaycl39pgunl",
   "metadata": {
    "id": "Iaycl39pgunl"
   },
   "source": [
    "### Feature selection using SelectionKBest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "95b53ea9",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "executionInfo": {
     "elapsed": 683,
     "status": "ok",
     "timestamp": 1686345159098,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "95b53ea9",
    "outputId": "db234750-cc25-4a17-a24c-74c4da279f3f"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.feature_selection import SelectKBest, chi2\n",
    "\n",
    "\n",
    "# Split the data into features and target variable\n",
    "X = dataset.drop('Outcome', axis=1)\n",
    "y = dataset['Outcome']\n",
    "\n",
    "# Create a SelectKBest object\n",
    "selector = SelectKBest(score_func=chi2, k=8)\n",
    "\n",
    "# Fit the selector to the data\n",
    "selector.fit(X, y)\n",
    "\n",
    "# Get the indices of the selected features\n",
    "selected_features = selector.get_support()\n",
    "\n",
    "# Print the names of the selected features\n",
    "# print(X.columns[selected_features])\n",
    "\n",
    "# Get the names of the selected features\n",
    "feature_names = X.columns[selected_features]\n",
    "\n",
    "# Plotting the bar diagram\n",
    "scores = selector.scores_[selected_features]\n",
    "y_pos = np.arange(len(feature_names))\n",
    "\n",
    "\n",
    "plt.barh(y_pos, scores, align='center', color='blue')\n",
    "plt.yticks(y_pos, feature_names)\n",
    "plt.xlabel('Chi-Squared Score')\n",
    "\n",
    "plt.title('Feature Importance')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "he9AVg1uKlgF",
   "metadata": {
    "id": "he9AVg1uKlgF"
   },
   "source": [
    "# Determining Test Size Using Cross Validation Score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ywsKNLOJe-Vt",
   "metadata": {
    "id": "ywsKNLOJe-Vt"
   },
   "source": [
    "### Checking the ratio of Target attribute in dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0241b189",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 428
    },
    "executionInfo": {
     "elapsed": 645,
     "status": "ok",
     "timestamp": 1686345159740,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "0241b189",
    "outputId": "d3dbda6c-3d6c-4d65-cf46-e936206bf29d"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Count the frequency of each price range category\n",
    "counts = dataset['Outcome'].value_counts()\n",
    "\n",
    "# Create a pie chart\n",
    "plt.pie(counts.values, labels=['Diabetes Negative','Diabetes Positive'], autopct='%1.1f%%')\n",
    "plt.title('Distribution of Diabetes Paitent ')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b9c40983",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 11,
     "status": "ok",
     "timestamp": 1686345159741,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "b9c40983",
    "outputId": "9228acba-8c94-478d-d6bb-10955fbd1e49"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    500\n",
       "1.0    268\n",
       "Name: Outcome, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = dataset['Outcome'].value_counts()\n",
    "counts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "JQnY171HxTcf",
   "metadata": {
    "id": "JQnY171HxTcf"
   },
   "source": [
    "### Observing Decision Tree Test Size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c1fec094",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 165
    },
    "executionInfo": {
     "elapsed": 3050,
     "status": "ok",
     "timestamp": 1686345162786,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "c1fec094",
    "outputId": "bd2d8f6c-5549-44fb-9e84-e65166dcc1ce"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results for Decision Tree Classifier:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_b6b6b_row0_col0, #T_b6b6b_row1_col0, #T_b6b6b_row2_col6, #T_b6b6b_row3_col0, #T_b6b6b_row4_col0 {\n",
       "  background-color: #f46d43;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_b6b6b\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_b6b6b_level0_col0\" class=\"col_heading level0 col0\" >Test Size 0.10</th>\n",
       "      <th id=\"T_b6b6b_level0_col1\" class=\"col_heading level0 col1\" >Test Size 0.15</th>\n",
       "      <th id=\"T_b6b6b_level0_col2\" class=\"col_heading level0 col2\" >Test Size 0.20</th>\n",
       "      <th id=\"T_b6b6b_level0_col3\" class=\"col_heading level0 col3\" >Test Size 0.25</th>\n",
       "      <th id=\"T_b6b6b_level0_col4\" class=\"col_heading level0 col4\" >Test Size 0.30</th>\n",
       "      <th id=\"T_b6b6b_level0_col5\" class=\"col_heading level0 col5\" >Test Size 0.35</th>\n",
       "      <th id=\"T_b6b6b_level0_col6\" class=\"col_heading level0 col6\" >Test Size 0.40</th>\n",
       "      <th id=\"T_b6b6b_level0_col7\" class=\"col_heading level0 col7\" >Test Size 0.45</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_b6b6b_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_b6b6b_row0_col0\" class=\"data row0 col0\" >0.717767</td>\n",
       "      <td id=\"T_b6b6b_row0_col1\" class=\"data row0 col1\" >0.700857</td>\n",
       "      <td id=\"T_b6b6b_row0_col2\" class=\"data row0 col2\" >0.693762</td>\n",
       "      <td id=\"T_b6b6b_row0_col3\" class=\"data row0 col3\" >0.697391</td>\n",
       "      <td id=\"T_b6b6b_row0_col4\" class=\"data row0 col4\" >0.690810</td>\n",
       "      <td id=\"T_b6b6b_row0_col5\" class=\"data row0 col5\" >0.667172</td>\n",
       "      <td id=\"T_b6b6b_row0_col6\" class=\"data row0 col6\" >0.700000</td>\n",
       "      <td id=\"T_b6b6b_row0_col7\" class=\"data row0 col7\" >0.670728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b6b6b_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_b6b6b_row1_col0\" class=\"data row1 col0\" >0.719278</td>\n",
       "      <td id=\"T_b6b6b_row1_col1\" class=\"data row1 col1\" >0.702408</td>\n",
       "      <td id=\"T_b6b6b_row1_col2\" class=\"data row1 col2\" >0.690577</td>\n",
       "      <td id=\"T_b6b6b_row1_col3\" class=\"data row1 col3\" >0.674783</td>\n",
       "      <td id=\"T_b6b6b_row1_col4\" class=\"data row1 col4\" >0.711250</td>\n",
       "      <td id=\"T_b6b6b_row1_col5\" class=\"data row1 col5\" >0.695273</td>\n",
       "      <td id=\"T_b6b6b_row1_col6\" class=\"data row1 col6\" >0.693478</td>\n",
       "      <td id=\"T_b6b6b_row1_col7\" class=\"data row1 col7\" >0.661232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b6b6b_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_b6b6b_row2_col0\" class=\"data row2 col0\" >0.719237</td>\n",
       "      <td id=\"T_b6b6b_row2_col1\" class=\"data row2 col1\" >0.705473</td>\n",
       "      <td id=\"T_b6b6b_row2_col2\" class=\"data row2 col2\" >0.690550</td>\n",
       "      <td id=\"T_b6b6b_row2_col3\" class=\"data row2 col3\" >0.685217</td>\n",
       "      <td id=\"T_b6b6b_row2_col4\" class=\"data row2 col4\" >0.700121</td>\n",
       "      <td id=\"T_b6b6b_row2_col5\" class=\"data row2 col5\" >0.689293</td>\n",
       "      <td id=\"T_b6b6b_row2_col6\" class=\"data row2 col6\" >0.739130</td>\n",
       "      <td id=\"T_b6b6b_row2_col7\" class=\"data row2 col7\" >0.661289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b6b6b_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_b6b6b_row3_col0\" class=\"data row3 col0\" >0.723564</td>\n",
       "      <td id=\"T_b6b6b_row3_col1\" class=\"data row3 col1\" >0.703911</td>\n",
       "      <td id=\"T_b6b6b_row3_col2\" class=\"data row3 col2\" >0.690524</td>\n",
       "      <td id=\"T_b6b6b_row3_col3\" class=\"data row3 col3\" >0.681739</td>\n",
       "      <td id=\"T_b6b6b_row3_col4\" class=\"data row3 col4\" >0.698287</td>\n",
       "      <td id=\"T_b6b6b_row3_col5\" class=\"data row3 col5\" >0.695212</td>\n",
       "      <td id=\"T_b6b6b_row3_col6\" class=\"data row3 col6\" >0.702174</td>\n",
       "      <td id=\"T_b6b6b_row3_col7\" class=\"data row3 col7\" >0.677815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b6b6b_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_b6b6b_row4_col0\" class=\"data row4 col0\" >0.714910</td>\n",
       "      <td id=\"T_b6b6b_row4_col1\" class=\"data row4 col1\" >0.710076</td>\n",
       "      <td id=\"T_b6b6b_row4_col2\" class=\"data row4 col2\" >0.700280</td>\n",
       "      <td id=\"T_b6b6b_row4_col3\" class=\"data row4 col3\" >0.685217</td>\n",
       "      <td id=\"T_b6b6b_row4_col4\" class=\"data row4 col4\" >0.694548</td>\n",
       "      <td id=\"T_b6b6b_row4_col5\" class=\"data row4 col5\" >0.681293</td>\n",
       "      <td id=\"T_b6b6b_row4_col6\" class=\"data row4 col6\" >0.710870</td>\n",
       "      <td id=\"T_b6b6b_row4_col7\" class=\"data row4 col7\" >0.670784</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from IPython.display import HTML\n",
    "\n",
    "# Load data from CSV file\n",
    "# ds = pd.read_csv('/content/drive/MyDrive/cse445 dataset/diabetes.csv')\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "X = dataset.drop('Outcome', axis=1)\n",
    "y = dataset['Outcome']\n",
    "\n",
    "# Define range of test sizes to try\n",
    "test_sizes = np.arange(0.1, 0.5, 0.05)\n",
    "\n",
    "# Create a decision tree classifier\n",
    "clf = DecisionTreeClassifier()\n",
    "\n",
    "# Repeat the cross-validation 5 times and store the results in a list\n",
    "n_runs = 5\n",
    "results = []\n",
    "for run in range(n_runs):\n",
    "    scores = []\n",
    "    for test_size in test_sizes:\n",
    "        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=42)\n",
    "        score = np.mean(cross_val_score(clf, X_train, y_train, cv=5))\n",
    "        scores.append(score)\n",
    "    results.append(scores)\n",
    "\n",
    "# Convert the results to a dataframe\n",
    "results_df = pd.DataFrame(results, columns=[f\"Test Size {ts:.2f}\" for ts in test_sizes])\n",
    "\n",
    "# Highlight the highest result in each row\n",
    "results_highlighted = results_df.style.highlight_max(color='#f46d43', axis=1)\n",
    "\n",
    "# Print the highlighted results in a table format\n",
    "print(\"Results for Decision Tree Classifier:\")\n",
    "html = results_highlighted.render()\n",
    "display(HTML(html))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "GmvbbM-bK_zm",
   "metadata": {
    "id": "GmvbbM-bK_zm"
   },
   "source": [
    "### Observing K-Nearest Neighbors Test Size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4a192d88",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 165
    },
    "executionInfo": {
     "elapsed": 4335,
     "status": "ok",
     "timestamp": 1686345167118,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "4a192d88",
    "outputId": "36012ba2-1c48-4050-f4ac-8af8b11557f9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-Nearest Neighbors Algorithm:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_75628_row0_col1, #T_75628_row1_col1, #T_75628_row2_col1, #T_75628_row3_col1, #T_75628_row4_col1 {\n",
       "  background-color: #f46d43;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_75628\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_75628_level0_col0\" class=\"col_heading level0 col0\" >Test Size 0.10</th>\n",
       "      <th id=\"T_75628_level0_col1\" class=\"col_heading level0 col1\" >Test Size 0.15</th>\n",
       "      <th id=\"T_75628_level0_col2\" class=\"col_heading level0 col2\" >Test Size 0.20</th>\n",
       "      <th id=\"T_75628_level0_col3\" class=\"col_heading level0 col3\" >Test Size 0.25</th>\n",
       "      <th id=\"T_75628_level0_col4\" class=\"col_heading level0 col4\" >Test Size 0.30</th>\n",
       "      <th id=\"T_75628_level0_col5\" class=\"col_heading level0 col5\" >Test Size 0.35</th>\n",
       "      <th id=\"T_75628_level0_col6\" class=\"col_heading level0 col6\" >Test Size 0.40</th>\n",
       "      <th id=\"T_75628_level0_col7\" class=\"col_heading level0 col7\" >Test Size 0.45</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_75628_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_75628_row0_col0\" class=\"data row0 col0\" >0.754009</td>\n",
       "      <td id=\"T_75628_row0_col1\" class=\"data row0 col1\" >0.765308</td>\n",
       "      <td id=\"T_75628_row0_col2\" class=\"data row0 col2\" >0.755764</td>\n",
       "      <td id=\"T_75628_row0_col3\" class=\"data row0 col3\" >0.756522</td>\n",
       "      <td id=\"T_75628_row0_col4\" class=\"data row0 col4\" >0.761578</td>\n",
       "      <td id=\"T_75628_row0_col5\" class=\"data row0 col5\" >0.753475</td>\n",
       "      <td id=\"T_75628_row0_col6\" class=\"data row0 col6\" >0.717391</td>\n",
       "      <td id=\"T_75628_row0_col7\" class=\"data row0 col7\" >0.727563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_75628_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_75628_row1_col0\" class=\"data row1 col0\" >0.754009</td>\n",
       "      <td id=\"T_75628_row1_col1\" class=\"data row1 col1\" >0.765308</td>\n",
       "      <td id=\"T_75628_row1_col2\" class=\"data row1 col2\" >0.755764</td>\n",
       "      <td id=\"T_75628_row1_col3\" class=\"data row1 col3\" >0.756522</td>\n",
       "      <td id=\"T_75628_row1_col4\" class=\"data row1 col4\" >0.761578</td>\n",
       "      <td id=\"T_75628_row1_col5\" class=\"data row1 col5\" >0.753475</td>\n",
       "      <td id=\"T_75628_row1_col6\" class=\"data row1 col6\" >0.717391</td>\n",
       "      <td id=\"T_75628_row1_col7\" class=\"data row1 col7\" >0.727563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_75628_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_75628_row2_col0\" class=\"data row2 col0\" >0.754009</td>\n",
       "      <td id=\"T_75628_row2_col1\" class=\"data row2 col1\" >0.765308</td>\n",
       "      <td id=\"T_75628_row2_col2\" class=\"data row2 col2\" >0.755764</td>\n",
       "      <td id=\"T_75628_row2_col3\" class=\"data row2 col3\" >0.756522</td>\n",
       "      <td id=\"T_75628_row2_col4\" class=\"data row2 col4\" >0.761578</td>\n",
       "      <td id=\"T_75628_row2_col5\" class=\"data row2 col5\" >0.753475</td>\n",
       "      <td id=\"T_75628_row2_col6\" class=\"data row2 col6\" >0.717391</td>\n",
       "      <td id=\"T_75628_row2_col7\" class=\"data row2 col7\" >0.727563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_75628_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_75628_row3_col0\" class=\"data row3 col0\" >0.754009</td>\n",
       "      <td id=\"T_75628_row3_col1\" class=\"data row3 col1\" >0.765308</td>\n",
       "      <td id=\"T_75628_row3_col2\" class=\"data row3 col2\" >0.755764</td>\n",
       "      <td id=\"T_75628_row3_col3\" class=\"data row3 col3\" >0.756522</td>\n",
       "      <td id=\"T_75628_row3_col4\" class=\"data row3 col4\" >0.761578</td>\n",
       "      <td id=\"T_75628_row3_col5\" class=\"data row3 col5\" >0.753475</td>\n",
       "      <td id=\"T_75628_row3_col6\" class=\"data row3 col6\" >0.717391</td>\n",
       "      <td id=\"T_75628_row3_col7\" class=\"data row3 col7\" >0.727563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_75628_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_75628_row4_col0\" class=\"data row4 col0\" >0.754009</td>\n",
       "      <td id=\"T_75628_row4_col1\" class=\"data row4 col1\" >0.765308</td>\n",
       "      <td id=\"T_75628_row4_col2\" class=\"data row4 col2\" >0.755764</td>\n",
       "      <td id=\"T_75628_row4_col3\" class=\"data row4 col3\" >0.756522</td>\n",
       "      <td id=\"T_75628_row4_col4\" class=\"data row4 col4\" >0.761578</td>\n",
       "      <td id=\"T_75628_row4_col5\" class=\"data row4 col5\" >0.753475</td>\n",
       "      <td id=\"T_75628_row4_col6\" class=\"data row4 col6\" >0.717391</td>\n",
       "      <td id=\"T_75628_row4_col7\" class=\"data row4 col7\" >0.727563</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Load data from CSV file\n",
    "# ds = pd.read_csv('/content/drive/MyDrive/cse445 dataset/diabetes.csv')\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "X = dataset.drop('Outcome', axis=1)\n",
    "y = dataset['Outcome']\n",
    "\n",
    "\n",
    "# Define range of test sizes to try\n",
    "test_sizes = np.arange(0.1, 0.5, 0.05)\n",
    "\n",
    "# Create a K Nearest Neighbors classifier\n",
    "clf = KNeighborsClassifier()\n",
    "\n",
    "# Create a standard scaler object\n",
    "std = StandardScaler()\n",
    "\n",
    "# Repeat the cross-validation 5 times and store the results in a list\n",
    "n_runs = 5\n",
    "results = []\n",
    "for run in range(n_runs):\n",
    "    scores = []\n",
    "    for test_size in test_sizes:\n",
    "        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=42)\n",
    "        # Scale the data using the standard scaler object\n",
    "        X_train_std = std.fit_transform(X_train)\n",
    "        X_test_std = std.transform(X_test)\n",
    "        clf.fit(X_train_std, y_train)\n",
    "        score = np.mean(cross_val_score(clf, X_train_std, y_train, cv=5))\n",
    "        scores.append(score)\n",
    "    results.append(scores)\n",
    "\n",
    "# Convert the results to a dataframe\n",
    "results_df = pd.DataFrame(results, columns=[f\"Test Size {ts:.2f}\" for ts in test_sizes])\n",
    "\n",
    "# Highlight the highest result in each row\n",
    "results_highlighted = results_df.style.highlight_max(color='#f46d43', axis=1)\n",
    "\n",
    "# Print the highlighted results in a table format\n",
    "print(\"K-Nearest Neighbors Algorithm:\")\n",
    "html = results_highlighted.render()\n",
    "display(HTML(html))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "GXKn-0IhK0T2",
   "metadata": {
    "id": "GXKn-0IhK0T2"
   },
   "source": [
    "### Observing Multinomial Naive Bayes Test Size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b6cae702",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 165
    },
    "executionInfo": {
     "elapsed": 5373,
     "status": "ok",
     "timestamp": 1686345172485,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "b6cae702",
    "outputId": "ddabd85d-f336-4205-f9ef-e4b0c009aab5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multinomial Naive Bayes Algorithm:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_d241f_row0_col0, #T_d241f_row1_col0, #T_d241f_row2_col0, #T_d241f_row3_col0, #T_d241f_row4_col0 {\n",
       "  background-color: #f46d43;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_d241f\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_d241f_level0_col0\" class=\"col_heading level0 col0\" >Test Size 0.10</th>\n",
       "      <th id=\"T_d241f_level0_col1\" class=\"col_heading level0 col1\" >Test Size 0.15</th>\n",
       "      <th id=\"T_d241f_level0_col2\" class=\"col_heading level0 col2\" >Test Size 0.20</th>\n",
       "      <th id=\"T_d241f_level0_col3\" class=\"col_heading level0 col3\" >Test Size 0.25</th>\n",
       "      <th id=\"T_d241f_level0_col4\" class=\"col_heading level0 col4\" >Test Size 0.30</th>\n",
       "      <th id=\"T_d241f_level0_col5\" class=\"col_heading level0 col5\" >Test Size 0.35</th>\n",
       "      <th id=\"T_d241f_level0_col6\" class=\"col_heading level0 col6\" >Test Size 0.40</th>\n",
       "      <th id=\"T_d241f_level0_col7\" class=\"col_heading level0 col7\" >Test Size 0.45</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_d241f_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_d241f_row0_col0\" class=\"data row0 col0\" >0.664269</td>\n",
       "      <td id=\"T_d241f_row0_col1\" class=\"data row0 col1\" >0.658027</td>\n",
       "      <td id=\"T_d241f_row0_col2\" class=\"data row0 col2\" >0.661282</td>\n",
       "      <td id=\"T_d241f_row0_col3\" class=\"data row0 col3\" >0.660870</td>\n",
       "      <td id=\"T_d241f_row0_col4\" class=\"data row0 col4\" >0.661215</td>\n",
       "      <td id=\"T_d241f_row0_col5\" class=\"data row0 col5\" >0.651434</td>\n",
       "      <td id=\"T_d241f_row0_col6\" class=\"data row0 col6\" >0.641304</td>\n",
       "      <td id=\"T_d241f_row0_col7\" class=\"data row0 col7\" >0.637619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d241f_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_d241f_row1_col0\" class=\"data row1 col0\" >0.664269</td>\n",
       "      <td id=\"T_d241f_row1_col1\" class=\"data row1 col1\" >0.658027</td>\n",
       "      <td id=\"T_d241f_row1_col2\" class=\"data row1 col2\" >0.661282</td>\n",
       "      <td id=\"T_d241f_row1_col3\" class=\"data row1 col3\" >0.660870</td>\n",
       "      <td id=\"T_d241f_row1_col4\" class=\"data row1 col4\" >0.661215</td>\n",
       "      <td id=\"T_d241f_row1_col5\" class=\"data row1 col5\" >0.651434</td>\n",
       "      <td id=\"T_d241f_row1_col6\" class=\"data row1 col6\" >0.641304</td>\n",
       "      <td id=\"T_d241f_row1_col7\" class=\"data row1 col7\" >0.637619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d241f_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_d241f_row2_col0\" class=\"data row2 col0\" >0.664269</td>\n",
       "      <td id=\"T_d241f_row2_col1\" class=\"data row2 col1\" >0.658027</td>\n",
       "      <td id=\"T_d241f_row2_col2\" class=\"data row2 col2\" >0.661282</td>\n",
       "      <td id=\"T_d241f_row2_col3\" class=\"data row2 col3\" >0.660870</td>\n",
       "      <td id=\"T_d241f_row2_col4\" class=\"data row2 col4\" >0.661215</td>\n",
       "      <td id=\"T_d241f_row2_col5\" class=\"data row2 col5\" >0.651434</td>\n",
       "      <td id=\"T_d241f_row2_col6\" class=\"data row2 col6\" >0.641304</td>\n",
       "      <td id=\"T_d241f_row2_col7\" class=\"data row2 col7\" >0.637619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d241f_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_d241f_row3_col0\" class=\"data row3 col0\" >0.664269</td>\n",
       "      <td id=\"T_d241f_row3_col1\" class=\"data row3 col1\" >0.658027</td>\n",
       "      <td id=\"T_d241f_row3_col2\" class=\"data row3 col2\" >0.661282</td>\n",
       "      <td id=\"T_d241f_row3_col3\" class=\"data row3 col3\" >0.660870</td>\n",
       "      <td id=\"T_d241f_row3_col4\" class=\"data row3 col4\" >0.661215</td>\n",
       "      <td id=\"T_d241f_row3_col5\" class=\"data row3 col5\" >0.651434</td>\n",
       "      <td id=\"T_d241f_row3_col6\" class=\"data row3 col6\" >0.641304</td>\n",
       "      <td id=\"T_d241f_row3_col7\" class=\"data row3 col7\" >0.637619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d241f_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_d241f_row4_col0\" class=\"data row4 col0\" >0.664269</td>\n",
       "      <td id=\"T_d241f_row4_col1\" class=\"data row4 col1\" >0.658027</td>\n",
       "      <td id=\"T_d241f_row4_col2\" class=\"data row4 col2\" >0.661282</td>\n",
       "      <td id=\"T_d241f_row4_col3\" class=\"data row4 col3\" >0.660870</td>\n",
       "      <td id=\"T_d241f_row4_col4\" class=\"data row4 col4\" >0.661215</td>\n",
       "      <td id=\"T_d241f_row4_col5\" class=\"data row4 col5\" >0.651434</td>\n",
       "      <td id=\"T_d241f_row4_col6\" class=\"data row4 col6\" >0.641304</td>\n",
       "      <td id=\"T_d241f_row4_col7\" class=\"data row4 col7\" >0.637619</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "\n",
    "# Load data from CSV file\n",
    "# ds = pd.read_csv('/content/drive/MyDrive/cse445 dataset/diabetes.csv')\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "X = dataset.drop('Outcome', axis=1)\n",
    "y = dataset['Outcome']\n",
    "\n",
    "\n",
    "# Define range of test sizes to try\n",
    "test_sizes = np.arange(0.1, 0.5, 0.05)\n",
    "\n",
    "# Create a Multinomial Naive Bayes classifier\n",
    "clf = MultinomialNB()\n",
    "\n",
    "# Repeat the cross-validation 5 times and store the results in a list\n",
    "n_runs = 5\n",
    "results = []\n",
    "for run in range(n_runs):\n",
    "    scores = []\n",
    "    for test_size in test_sizes:\n",
    "        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=42)\n",
    "        clf.fit(X_train, y_train)\n",
    "        score = np.mean(cross_val_score(clf, X_train, y_train, cv=5))\n",
    "        scores.append(score)\n",
    "    results.append(scores)\n",
    "\n",
    "# Convert the results to a dataframe\n",
    "results_df = pd.DataFrame(results, columns=[f\"Test Size {ts:.2f}\" for ts in test_sizes])\n",
    "\n",
    "# Highlight the highest result in each row\n",
    "results_highlighted = results_df.style.highlight_max(color='#f46d43', axis=1)\n",
    "\n",
    "# Print the highlighted results in a table format\n",
    "print(\"Multinomial Naive Bayes Algorithm:\")\n",
    "html = results_highlighted.render()\n",
    "display(HTML(html))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "YqNd0RF_K2sF",
   "metadata": {
    "id": "YqNd0RF_K2sF"
   },
   "source": [
    "### Observing Artificial Neural Network Test Size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "222f9430",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 165
    },
    "executionInfo": {
     "elapsed": 451236,
     "status": "ok",
     "timestamp": 1686345623716,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "222f9430",
    "outputId": "0b1932d2-988f-4ffb-a9e9-a083f8f2ff22"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Artificial Neural Network (ANN):\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_8d466_row0_col5, #T_8d466_row1_col5, #T_8d466_row2_col5, #T_8d466_row3_col5, #T_8d466_row4_col5 {\n",
       "  background-color: #f46d43;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_8d466\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_8d466_level0_col0\" class=\"col_heading level0 col0\" >Test Size 0.10</th>\n",
       "      <th id=\"T_8d466_level0_col1\" class=\"col_heading level0 col1\" >Test Size 0.15</th>\n",
       "      <th id=\"T_8d466_level0_col2\" class=\"col_heading level0 col2\" >Test Size 0.20</th>\n",
       "      <th id=\"T_8d466_level0_col3\" class=\"col_heading level0 col3\" >Test Size 0.25</th>\n",
       "      <th id=\"T_8d466_level0_col4\" class=\"col_heading level0 col4\" >Test Size 0.30</th>\n",
       "      <th id=\"T_8d466_level0_col5\" class=\"col_heading level0 col5\" >Test Size 0.35</th>\n",
       "      <th id=\"T_8d466_level0_col6\" class=\"col_heading level0 col6\" >Test Size 0.40</th>\n",
       "      <th id=\"T_8d466_level0_col7\" class=\"col_heading level0 col7\" >Test Size 0.45</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_8d466_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_8d466_row0_col0\" class=\"data row0 col0\" >0.738046</td>\n",
       "      <td id=\"T_8d466_row0_col1\" class=\"data row0 col1\" >0.745332</td>\n",
       "      <td id=\"T_8d466_row0_col2\" class=\"data row0 col2\" >0.737758</td>\n",
       "      <td id=\"T_8d466_row0_col3\" class=\"data row0 col3\" >0.744348</td>\n",
       "      <td id=\"T_8d466_row0_col4\" class=\"data row0 col4\" >0.746677</td>\n",
       "      <td id=\"T_8d466_row0_col5\" class=\"data row0 col5\" >0.749455</td>\n",
       "      <td id=\"T_8d466_row0_col6\" class=\"data row0 col6\" >0.734783</td>\n",
       "      <td id=\"T_8d466_row0_col7\" class=\"data row0 col7\" >0.715742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8d466_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_8d466_row1_col0\" class=\"data row1 col0\" >0.738046</td>\n",
       "      <td id=\"T_8d466_row1_col1\" class=\"data row1 col1\" >0.745332</td>\n",
       "      <td id=\"T_8d466_row1_col2\" class=\"data row1 col2\" >0.737758</td>\n",
       "      <td id=\"T_8d466_row1_col3\" class=\"data row1 col3\" >0.744348</td>\n",
       "      <td id=\"T_8d466_row1_col4\" class=\"data row1 col4\" >0.746677</td>\n",
       "      <td id=\"T_8d466_row1_col5\" class=\"data row1 col5\" >0.749455</td>\n",
       "      <td id=\"T_8d466_row1_col6\" class=\"data row1 col6\" >0.734783</td>\n",
       "      <td id=\"T_8d466_row1_col7\" class=\"data row1 col7\" >0.715742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8d466_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_8d466_row2_col0\" class=\"data row2 col0\" >0.738046</td>\n",
       "      <td id=\"T_8d466_row2_col1\" class=\"data row2 col1\" >0.745332</td>\n",
       "      <td id=\"T_8d466_row2_col2\" class=\"data row2 col2\" >0.737758</td>\n",
       "      <td id=\"T_8d466_row2_col3\" class=\"data row2 col3\" >0.744348</td>\n",
       "      <td id=\"T_8d466_row2_col4\" class=\"data row2 col4\" >0.746677</td>\n",
       "      <td id=\"T_8d466_row2_col5\" class=\"data row2 col5\" >0.749455</td>\n",
       "      <td id=\"T_8d466_row2_col6\" class=\"data row2 col6\" >0.734783</td>\n",
       "      <td id=\"T_8d466_row2_col7\" class=\"data row2 col7\" >0.715742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8d466_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_8d466_row3_col0\" class=\"data row3 col0\" >0.738046</td>\n",
       "      <td id=\"T_8d466_row3_col1\" class=\"data row3 col1\" >0.745332</td>\n",
       "      <td id=\"T_8d466_row3_col2\" class=\"data row3 col2\" >0.737758</td>\n",
       "      <td id=\"T_8d466_row3_col3\" class=\"data row3 col3\" >0.744348</td>\n",
       "      <td id=\"T_8d466_row3_col4\" class=\"data row3 col4\" >0.746677</td>\n",
       "      <td id=\"T_8d466_row3_col5\" class=\"data row3 col5\" >0.749455</td>\n",
       "      <td id=\"T_8d466_row3_col6\" class=\"data row3 col6\" >0.734783</td>\n",
       "      <td id=\"T_8d466_row3_col7\" class=\"data row3 col7\" >0.715742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8d466_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_8d466_row4_col0\" class=\"data row4 col0\" >0.738046</td>\n",
       "      <td id=\"T_8d466_row4_col1\" class=\"data row4 col1\" >0.745332</td>\n",
       "      <td id=\"T_8d466_row4_col2\" class=\"data row4 col2\" >0.737758</td>\n",
       "      <td id=\"T_8d466_row4_col3\" class=\"data row4 col3\" >0.744348</td>\n",
       "      <td id=\"T_8d466_row4_col4\" class=\"data row4 col4\" >0.746677</td>\n",
       "      <td id=\"T_8d466_row4_col5\" class=\"data row4 col5\" >0.749455</td>\n",
       "      <td id=\"T_8d466_row4_col6\" class=\"data row4 col6\" >0.734783</td>\n",
       "      <td id=\"T_8d466_row4_col7\" class=\"data row4 col7\" >0.715742</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from IPython.display import display, HTML\n",
    "\n",
    "# Load data from CSV file\n",
    "# ds = pd.read_csv('/content/drive/MyDrive/cse445 dataset/diabetes.csv')\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "X = dataset.drop('Outcome', axis=1)\n",
    "y = dataset['Outcome']\n",
    "\n",
    "# Define range of test sizes to try\n",
    "test_sizes = np.arange(0.1, 0.5, 0.05)\n",
    "\n",
    "# Create an MLPClassifier (Artificial Neural Network)\n",
    "clf = MLPClassifier(hidden_layer_sizes=(10, 10), max_iter=1000, random_state=42)\n",
    "\n",
    "# Create a standard scaler object\n",
    "std = StandardScaler()\n",
    "\n",
    "# Repeat the cross-validation 5 times and store the results in a list\n",
    "n_runs = 5\n",
    "results = []\n",
    "for run in range(n_runs):\n",
    "    scores = []\n",
    "    for test_size in test_sizes:\n",
    "        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=42)\n",
    "        # Scale the data using the standard scaler object\n",
    "        X_train_std = std.fit_transform(X_train)\n",
    "        X_test_std = std.transform(X_test)\n",
    "        clf.fit(X_train_std, y_train)\n",
    "        score = np.mean(cross_val_score(clf, X_train_std, y_train, cv=5))\n",
    "        scores.append(score)\n",
    "    results.append(scores)\n",
    "\n",
    "# Convert the results to a dataframe\n",
    "results_df = pd.DataFrame(results, columns=[f\"Test Size {ts:.2f}\" for ts in test_sizes])\n",
    "\n",
    "# Highlight the highest result in each row\n",
    "results_highlighted = results_df.style.highlight_max(color='#f46d43', axis=1)\n",
    "\n",
    "# Print the highlighted results in a table format\n",
    "print(\"Artificial Neural Network (ANN):\")\n",
    "html = results_highlighted.render()\n",
    "display(HTML(html))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "OWWZ3r0_lYwZ",
   "metadata": {
    "id": "OWWZ3r0_lYwZ"
   },
   "source": [
    "# Training the Models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cUZUYh-kmsjO",
   "metadata": {
    "id": "cUZUYh-kmsjO"
   },
   "source": [
    "### Separating the training and testing sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8u9HTE0T9_yN",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 52,
     "status": "ok",
     "timestamp": 1686345623717,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "8u9HTE0T9_yN",
    "outputId": "b41e6ab1-bb0d-4c94-d699-4393cde12661"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-fe94c4b8-b294-4bd0-a7d4-627ef0f69920\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.6270</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.3510</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>29.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.6720</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.1670</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>43.1</td>\n",
       "      <td>1.3884</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fe94c4b8-b294-4bd0-a7d4-627ef0f69920')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-fe94c4b8-b294-4bd0-a7d4-627ef0f69920 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-fe94c4b8-b294-4bd0-a7d4-627ef0f69920');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72           35.0    147.0  33.6   \n",
       "1            1       85             66           29.0    147.0  26.6   \n",
       "2            8      183             64           29.0    147.0  23.3   \n",
       "3            1       89             66           23.0     94.0  28.1   \n",
       "4            0      137             40           35.0    168.0  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction   Age  Outcome  \n",
       "0                    0.6270  50.0      1.0  \n",
       "1                    0.3510  31.0      0.0  \n",
       "2                    0.6720  32.0      1.0  \n",
       "3                    0.1670  21.0      0.0  \n",
       "4                    1.3884  33.0      1.0  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "df1a377e",
   "metadata": {
    "executionInfo": {
     "elapsed": 49,
     "status": "ok",
     "timestamp": 1686345623717,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "df1a377e"
   },
   "outputs": [],
   "source": [
    "# Split dataset into input features (X) and target variable (y)\n",
    "X = dataset.drop('Outcome', axis=1)\n",
    "y = dataset['Outcome']\n",
    "\n",
    "# Split data into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "lxiO74CVnadV",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 458
    },
    "executionInfo": {
     "elapsed": 49,
     "status": "ok",
     "timestamp": 1686345623718,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "lxiO74CVnadV",
    "outputId": "e9c9ef67-ca67-40b5-94e5-95e082264553"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Printing Features: \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-b4a57f16-eb1d-41f8-98fa-f075a6be9f0d\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.6270</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.3510</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>29.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.6720</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.1670</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>43.1</td>\n",
       "      <td>1.3884</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>10</td>\n",
       "      <td>101</td>\n",
       "      <td>76</td>\n",
       "      <td>48.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.1710</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>2</td>\n",
       "      <td>122</td>\n",
       "      <td>70</td>\n",
       "      <td>27.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>36.8</td>\n",
       "      <td>0.3400</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>5</td>\n",
       "      <td>121</td>\n",
       "      <td>72</td>\n",
       "      <td>23.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.2450</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>1</td>\n",
       "      <td>126</td>\n",
       "      <td>60</td>\n",
       "      <td>29.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.3490</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>70</td>\n",
       "      <td>31.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.3150</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>768 rows × 8 columns</p>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b4a57f16-eb1d-41f8-98fa-f075a6be9f0d')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-b4a57f16-eb1d-41f8-98fa-f075a6be9f0d button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-b4a57f16-eb1d-41f8-98fa-f075a6be9f0d');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0              6      148             72           35.0    147.0  33.6   \n",
       "1              1       85             66           29.0    147.0  26.6   \n",
       "2              8      183             64           29.0    147.0  23.3   \n",
       "3              1       89             66           23.0     94.0  28.1   \n",
       "4              0      137             40           35.0    168.0  43.1   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "763           10      101             76           48.0    180.0  32.9   \n",
       "764            2      122             70           27.0    147.0  36.8   \n",
       "765            5      121             72           23.0    112.0  26.2   \n",
       "766            1      126             60           29.0    147.0  30.1   \n",
       "767            1       93             70           31.0    147.0  30.4   \n",
       "\n",
       "     DiabetesPedigreeFunction   Age  \n",
       "0                      0.6270  50.0  \n",
       "1                      0.3510  31.0  \n",
       "2                      0.6720  32.0  \n",
       "3                      0.1670  21.0  \n",
       "4                      1.3884  33.0  \n",
       "..                        ...   ...  \n",
       "763                    0.1710  63.0  \n",
       "764                    0.3400  27.0  \n",
       "765                    0.2450  30.0  \n",
       "766                    0.3490  47.0  \n",
       "767                    0.3150  23.0  \n",
       "\n",
       "[768 rows x 8 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Printing Features: \\n\")\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "Syk2UdVsnpPP",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 43,
     "status": "ok",
     "timestamp": 1686345623718,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "Syk2UdVsnpPP",
    "outputId": "7b3ed98a-f41e-4e69-80c5-afefbe2b959e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Printing Target Attribute: \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0      1.0\n",
       "1      0.0\n",
       "2      1.0\n",
       "3      0.0\n",
       "4      1.0\n",
       "      ... \n",
       "763    0.0\n",
       "764    0.0\n",
       "765    0.0\n",
       "766    1.0\n",
       "767    0.0\n",
       "Name: Outcome, Length: 768, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Printing Target Attribute: \\n\")\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "_-3QcOn_3nSi",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "executionInfo": {
     "elapsed": 40,
     "status": "ok",
     "timestamp": 1686345623719,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "_-3QcOn_3nSi",
    "outputId": "bb266d2d-0e83-46db-f68c-bf150634007c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-ae000065-f7e4-4965-93ad-1fecc3b41bba\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>2</td>\n",
       "      <td>84</td>\n",
       "      <td>27</td>\n",
       "      <td>29.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>8.05</td>\n",
       "      <td>0.304</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618</th>\n",
       "      <td>9</td>\n",
       "      <td>112</td>\n",
       "      <td>82</td>\n",
       "      <td>24.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>28.20</td>\n",
       "      <td>1.282</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>1</td>\n",
       "      <td>139</td>\n",
       "      <td>46</td>\n",
       "      <td>19.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>28.70</td>\n",
       "      <td>0.654</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>0</td>\n",
       "      <td>161</td>\n",
       "      <td>50</td>\n",
       "      <td>29.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>21.90</td>\n",
       "      <td>0.254</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>6</td>\n",
       "      <td>134</td>\n",
       "      <td>80</td>\n",
       "      <td>37.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>46.20</td>\n",
       "      <td>0.238</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>5</td>\n",
       "      <td>139</td>\n",
       "      <td>64</td>\n",
       "      <td>35.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>28.60</td>\n",
       "      <td>0.411</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>1</td>\n",
       "      <td>96</td>\n",
       "      <td>115</td>\n",
       "      <td>29.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>22.40</td>\n",
       "      <td>0.207</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>10</td>\n",
       "      <td>101</td>\n",
       "      <td>86</td>\n",
       "      <td>37.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>45.60</td>\n",
       "      <td>1.136</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>0</td>\n",
       "      <td>141</td>\n",
       "      <td>27</td>\n",
       "      <td>29.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>42.40</td>\n",
       "      <td>0.205</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>0</td>\n",
       "      <td>125</td>\n",
       "      <td>96</td>\n",
       "      <td>29.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>22.50</td>\n",
       "      <td>0.262</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>614 rows × 8 columns</p>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ae000065-f7e4-4965-93ad-1fecc3b41bba')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-ae000065-f7e4-4965-93ad-1fecc3b41bba button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-ae000065-f7e4-4965-93ad-1fecc3b41bba');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin    BMI  \\\n",
       "60             2       84             27           29.0    147.0   8.05   \n",
       "618            9      112             82           24.0    147.0  28.20   \n",
       "346            1      139             46           19.0     83.0  28.70   \n",
       "294            0      161             50           29.0    147.0  21.90   \n",
       "231            6      134             80           37.0    370.0  46.20   \n",
       "..           ...      ...            ...            ...      ...    ...   \n",
       "71             5      139             64           35.0    140.0  28.60   \n",
       "106            1       96            115           29.0    147.0  22.40   \n",
       "270           10      101             86           37.0    147.0  45.60   \n",
       "435            0      141             27           29.0    147.0  42.40   \n",
       "102            0      125             96           29.0    147.0  22.50   \n",
       "\n",
       "     DiabetesPedigreeFunction   Age  \n",
       "60                      0.304  21.0  \n",
       "618                     1.282  50.0  \n",
       "346                     0.654  22.0  \n",
       "294                     0.254  65.0  \n",
       "231                     0.238  46.0  \n",
       "..                        ...   ...  \n",
       "71                      0.411  26.0  \n",
       "106                     0.207  27.0  \n",
       "270                     1.136  38.0  \n",
       "435                     0.205  29.0  \n",
       "102                     0.262  21.0  \n",
       "\n",
       "[614 rows x 8 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "njiwIG4Y3uPR",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 39,
     "status": "ok",
     "timestamp": 1686345623719,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "njiwIG4Y3uPR",
    "outputId": "6dc36b53-bca6-4c4f-c1fd-2307058bc46e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60     0.0\n",
       "618    1.0\n",
       "346    0.0\n",
       "294    0.0\n",
       "231    1.0\n",
       "      ... \n",
       "71     0.0\n",
       "106    0.0\n",
       "270    1.0\n",
       "435    1.0\n",
       "102    0.0\n",
       "Name: Outcome, Length: 614, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ptF480ei3p5k",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "executionInfo": {
     "elapsed": 36,
     "status": "ok",
     "timestamp": 1686345623720,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "ptF480ei3p5k",
    "outputId": "d964953e-87cb-4327-fa13-9f4fd89fec48"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-20c2d93f-fb5d-4f05-891d-c83ecb384965\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>668</th>\n",
       "      <td>6</td>\n",
       "      <td>98</td>\n",
       "      <td>58</td>\n",
       "      <td>33.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.430</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>2</td>\n",
       "      <td>112</td>\n",
       "      <td>75</td>\n",
       "      <td>32.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>35.7</td>\n",
       "      <td>0.148</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624</th>\n",
       "      <td>2</td>\n",
       "      <td>108</td>\n",
       "      <td>64</td>\n",
       "      <td>29.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>30.8</td>\n",
       "      <td>0.158</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>690</th>\n",
       "      <td>8</td>\n",
       "      <td>107</td>\n",
       "      <td>80</td>\n",
       "      <td>29.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>24.6</td>\n",
       "      <td>0.856</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>7</td>\n",
       "      <td>136</td>\n",
       "      <td>90</td>\n",
       "      <td>29.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>29.9</td>\n",
       "      <td>0.210</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>9</td>\n",
       "      <td>165</td>\n",
       "      <td>88</td>\n",
       "      <td>29.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.302</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>534</th>\n",
       "      <td>1</td>\n",
       "      <td>77</td>\n",
       "      <td>56</td>\n",
       "      <td>30.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>33.3</td>\n",
       "      <td>1.251</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>8</td>\n",
       "      <td>95</td>\n",
       "      <td>72</td>\n",
       "      <td>29.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>36.8</td>\n",
       "      <td>0.485</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>2</td>\n",
       "      <td>146</td>\n",
       "      <td>70</td>\n",
       "      <td>38.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.337</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>462</th>\n",
       "      <td>8</td>\n",
       "      <td>74</td>\n",
       "      <td>70</td>\n",
       "      <td>40.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>35.3</td>\n",
       "      <td>0.705</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>154 rows × 8 columns</p>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-20c2d93f-fb5d-4f05-891d-c83ecb384965')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-20c2d93f-fb5d-4f05-891d-c83ecb384965 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-20c2d93f-fb5d-4f05-891d-c83ecb384965');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "668            6       98             58           33.0    190.0  34.0   \n",
       "324            2      112             75           32.0    147.0  35.7   \n",
       "624            2      108             64           29.0    147.0  30.8   \n",
       "690            8      107             80           29.0    147.0  24.6   \n",
       "473            7      136             90           29.0    147.0  29.9   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "355            9      165             88           29.0    147.0  30.4   \n",
       "534            1       77             56           30.0     56.0  33.3   \n",
       "344            8       95             72           29.0    147.0  36.8   \n",
       "296            2      146             70           38.0    360.0  28.0   \n",
       "462            8       74             70           40.0     49.0  35.3   \n",
       "\n",
       "     DiabetesPedigreeFunction   Age  \n",
       "668                     0.430  43.0  \n",
       "324                     0.148  21.0  \n",
       "624                     0.158  21.0  \n",
       "690                     0.856  34.0  \n",
       "473                     0.210  50.0  \n",
       "..                        ...   ...  \n",
       "355                     0.302  49.0  \n",
       "534                     1.251  24.0  \n",
       "344                     0.485  57.0  \n",
       "296                     0.337  29.0  \n",
       "462                     0.705  39.0  \n",
       "\n",
       "[154 rows x 8 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "O_-rbLnu3sCB",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 35,
     "status": "ok",
     "timestamp": 1686345623720,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "O_-rbLnu3sCB",
    "outputId": "2d756172-19c6-48ab-d2c8-1f85b823be8f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "668    0.0\n",
       "324    0.0\n",
       "624    0.0\n",
       "690    0.0\n",
       "473    0.0\n",
       "      ... \n",
       "355    1.0\n",
       "534    0.0\n",
       "344    0.0\n",
       "296    1.0\n",
       "462    0.0\n",
       "Name: Outcome, Length: 154, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5107bad7",
   "metadata": {
    "id": "5107bad7"
   },
   "source": [
    "### Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "cb3e1c90",
   "metadata": {
    "executionInfo": {
     "elapsed": 33,
     "status": "ok",
     "timestamp": 1686345623721,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "cb3e1c90"
   },
   "outputs": [],
   "source": [
    "sc_X = StandardScaler()\n",
    "X_train_std = sc_X.fit_transform(X_train)\n",
    "X_test_std = sc_X.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Uo5w3GTwH6cc",
   "metadata": {
    "id": "Uo5w3GTwH6cc"
   },
   "source": [
    "## Models Training & Classification Report "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Ekf2yKIzxCM3",
   "metadata": {
    "id": "Ekf2yKIzxCM3"
   },
   "source": [
    "### Decision Tree Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "CmueXkMrn4dd",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 32,
     "status": "ok",
     "timestamp": 1686345623721,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "CmueXkMrn4dd",
    "outputId": "8781cc81-5d76-43ee-817c-807bd67a7c0a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree Algorithm Accuracy: 0.7272727272727273\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "\n",
    "# Train decision tree classifier on training set\n",
    "DT_clf = DecisionTreeClassifier()\n",
    "DT_clf.fit(X_train, y_train)\n",
    "\n",
    "# Predict classes for test set\n",
    "DT_pred = DT_clf.predict(X_test)\n",
    "\n",
    "# Calculate accuracy of predictions\n",
    "DT_ACC = accuracy_score(y_test, DT_pred)\n",
    "print(f\"Decision Tree Algorithm Accuracy: {DT_ACC}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1BFWHTVXohWY",
   "metadata": {
    "id": "1BFWHTVXohWY"
   },
   "source": [
    "### Decision Tree Algorithm Classification report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "zk-1mQLYokC3",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 31,
     "status": "ok",
     "timestamp": 1686345623722,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "zk-1mQLYokC3",
    "outputId": "ca015ed4-0503-42ba-b38f-c01dd5681132"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree Algorithm Classification report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.79      0.78      0.79        99\n",
      "         1.0       0.61      0.64      0.62        55\n",
      "\n",
      "    accuracy                           0.73       154\n",
      "   macro avg       0.70      0.71      0.71       154\n",
      "weighted avg       0.73      0.73      0.73       154\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "# Generate classification report\n",
    "DT_report = classification_report(y_test, DT_pred)\n",
    "\n",
    "# Print the header\n",
    "print(\"Decision Tree Algorithm Classification report\")\n",
    "\n",
    "# Print the report\n",
    "print(DT_report)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "N8zjkA5dpcot",
   "metadata": {
    "id": "N8zjkA5dpcot"
   },
   "source": [
    "### K-Nearest Neighbors Algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "GVW40TTd9PFN",
   "metadata": {
    "id": "GVW40TTd9PFN"
   },
   "source": [
    "#### Find the value of K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "py3olHFl9VSF",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 29,
     "status": "ok",
     "timestamp": 1686345623723,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "py3olHFl9VSF",
    "outputId": "d698a8b3-807d-4d8d-8373-59405f1015bb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.409673645990857"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "math.sqrt(len(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f1E1B2BUpgDA",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 27,
     "status": "ok",
     "timestamp": 1686345623724,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "f1E1B2BUpgDA",
    "outputId": "822e9570-f438-44d9-a8c4-e92ac98c0868"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-Nearest Neighbors Algorithm Accuracy: 0.7532467532467533\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Train K-Nearest Neighbors classifier on training set\n",
    "# as 12 is an even no. we consider 11 as the value of K\n",
    "KNN_clf = KNeighborsClassifier(n_neighbors=11,p=2,metric='euclidean')       \n",
    "KNN_clf.fit(X_train_std, y_train)\n",
    "\n",
    "# Predict classes for test set\n",
    "KNN_pred = KNN_clf.predict(X_test_std)\n",
    "\n",
    "# Calculate accuracy of predictions\n",
    "KNN_ACC = accuracy_score(y_test, KNN_pred)\n",
    "print(f\"K-Nearest Neighbors Algorithm Accuracy: {KNN_ACC}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "nLmKgdpjpuA0",
   "metadata": {
    "id": "nLmKgdpjpuA0"
   },
   "source": [
    "### K-Nearest Neighbors Algorithm Classification report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "37Rx3WN5ps6l",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 24,
     "status": "ok",
     "timestamp": 1686345623724,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "37Rx3WN5ps6l",
    "outputId": "1f6b0354-ffb6-4cd0-e280-343795b69bef"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-Nearest Neighbors Algorithm Classification report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.80      0.82      0.81        99\n",
      "         1.0       0.66      0.64      0.65        55\n",
      "\n",
      "    accuracy                           0.75       154\n",
      "   macro avg       0.73      0.73      0.73       154\n",
      "weighted avg       0.75      0.75      0.75       154\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "# Generate classification report\n",
    "KNN_report = classification_report(y_test, KNN_pred)\n",
    "\n",
    "# Print the header\n",
    "print(\"K-Nearest Neighbors Algorithm Classification report\")\n",
    "\n",
    "# Print the report\n",
    "print(KNN_report)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "I9CrkxdZp000",
   "metadata": {
    "id": "I9CrkxdZp000"
   },
   "source": [
    "### Naive Bayes Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "IEizqYmEp4WY",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 23,
     "status": "ok",
     "timestamp": 1686345623725,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "IEizqYmEp4WY",
    "outputId": "76e23252-81bb-4c3f-e43c-5d773fb8c699"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes Algorithm Accuracy: 0.7077922077922078\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Train Naive Bayes classifier on training set\n",
    "NB_clf = MultinomialNB()\n",
    "NB_clf.fit(X_train, y_train)\n",
    "\n",
    "# Predict classes for test set\n",
    "NB_pred = NB_clf.predict(X_test)\n",
    "\n",
    "# Calculate accuracy of predictions\n",
    "NB_ACC = accuracy_score(y_test, NB_pred)\n",
    "print(f\"Naive Bayes Algorithm Accuracy: {NB_ACC}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "328g45Elp9_9",
   "metadata": {
    "id": "328g45Elp9_9"
   },
   "source": [
    "### Naive Bayes Algorithm Classification report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "Sz05P3YsqBQ8",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 563,
     "status": "ok",
     "timestamp": 1686345624267,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "Sz05P3YsqBQ8",
    "outputId": "f3970fcf-12c3-4f84-dc31-ae90bdc7a0da"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes Algorithm Classification report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.78      0.76      0.77        99\n",
      "         1.0       0.59      0.62      0.60        55\n",
      "\n",
      "    accuracy                           0.71       154\n",
      "   macro avg       0.68      0.69      0.69       154\n",
      "weighted avg       0.71      0.71      0.71       154\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "# Generate classification report\n",
    "NB_report = classification_report(y_test, NB_pred)\n",
    "\n",
    "# Print the header\n",
    "print(\"Naive Bayes Algorithm Classification report\")\n",
    "\n",
    "# Print the report\n",
    "print(NB_report)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "JBmbeZCLtepY",
   "metadata": {
    "id": "JBmbeZCLtepY"
   },
   "source": [
    "### Artificial Neural Network Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "rlmXvWuutgew",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 7222,
     "status": "ok",
     "timestamp": 1686345631486,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "rlmXvWuutgew",
    "outputId": "daf08d8e-aaa7-4c9d-f935-5982789b3d1f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANN Algorithm Accuracy: 0.6428571428571429\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Create and train the ANN classifier\n",
    "ANN_clf = MLPClassifier(hidden_layer_sizes=(50, 50), max_iter=1000, random_state=42)\n",
    "ANN_clf.fit(X_train_std, y_train)\n",
    "\n",
    "# Predict classes for test set\n",
    "ANN_pred = ANN_clf.predict(X_test_std)\n",
    "\n",
    "# Calculate accuracy of predictions\n",
    "ANN_ACC = accuracy_score(y_test, ANN_pred)\n",
    "print(f\"ANN Algorithm Accuracy: {ANN_ACC}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "GVXfuaiiuMXB",
   "metadata": {
    "id": "GVXfuaiiuMXB"
   },
   "source": [
    "### Artificial Neural Network Algorithm Classification report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "KQ16NjlsuP9Z",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 23,
     "status": "ok",
     "timestamp": 1686345631486,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "KQ16NjlsuP9Z",
    "outputId": "d83772a8-8837-47db-85f1-ecde020ca148"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Artificial Neural Network Algorithm Classification report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.72      0.73      0.72        99\n",
      "         1.0       0.50      0.49      0.50        55\n",
      "\n",
      "    accuracy                           0.64       154\n",
      "   macro avg       0.61      0.61      0.61       154\n",
      "weighted avg       0.64      0.64      0.64       154\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "# Generate classification report\n",
    "ANN_report = classification_report(y_test, ANN_pred)\n",
    "\n",
    "# Print the header\n",
    "print(\"Artificial Neural Network Algorithm Classification report\")\n",
    "\n",
    "# Print the report\n",
    "print(ANN_report)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dn7XNyfIyFBN",
   "metadata": {
    "id": "dn7XNyfIyFBN"
   },
   "source": [
    "### Accuracy Comparison of Four Algorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "ouA2MkdIzCqv",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 573
    },
    "executionInfo": {
     "elapsed": 906,
     "status": "ok",
     "timestamp": 1686345632378,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "ouA2MkdIzCqv",
    "outputId": "75c78456-a986-46b2-9eb5-66f0c56ccfab"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "algorithms = ['Decision Tree', 'K-Nearest Neighbors', 'Naive Bayes', 'Artificial Neural Network']\n",
    "accuracy_scores = [DT_ACC, KNN_ACC, NB_ACC,  ANN_ACC]\n",
    "\n",
    "sns.set(style='whitegrid')\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "ax = sns.barplot(x=algorithms, y=accuracy_scores)\n",
    "ax.set_title('Comparison of Four Algorithms')\n",
    "ax.set_xlabel('Algorithms')\n",
    "ax.set_ylabel('Accuracy Score')\n",
    "\n",
    "for i, v in enumerate(accuracy_scores):\n",
    "    ax.text(i, v + 0.005, str(round(v, 3)), fontsize=12, color='black', ha='center')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0hBvE_0NzVBt",
   "metadata": {
    "id": "0hBvE_0NzVBt"
   },
   "source": [
    "### Creating a Custom Function for Comparing Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "eYigw3Cnzfsj",
   "metadata": {
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1686345632378,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "eYigw3Cnzfsj"
   },
   "outputs": [],
   "source": [
    "# Metrics\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "\n",
    "def metrics_calculator(y_test, y_pred, model_name):\n",
    "    '''\n",
    "    This function calculates all desired performance metrics for a given model.\n",
    "    '''\n",
    "    result = pd.DataFrame(data=[accuracy_score(y_test, y_pred),\n",
    "                                precision_score(y_test, y_pred, average='macro'),\n",
    "                                recall_score(y_test, y_pred, average='macro'),\n",
    "                                f1_score(y_test, y_pred, average='macro')],\n",
    "                          index=['Accuracy','Precision','Recall','F1-score'],\n",
    "                          columns=[model_name])\n",
    "    \n",
    "    # Adding background gradient and color scale\n",
    "    cmap = 'YlGnBu'  # Choose colormap\n",
    "    vmin = result.min().min()\n",
    "    vmax = result.max().max()\n",
    "    styled_result = result.style.background_gradient(cmap=cmap, vmin=vmin, vmax=vmax)\n",
    "\n",
    "    return styled_result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "gec512SB0fbz",
   "metadata": {
    "id": "gec512SB0fbz"
   },
   "source": [
    "### Now using the above function calculating Accuracy,Precision,Recall,F1-score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "Vez-3UgFzglc",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 196
    },
    "executionInfo": {
     "elapsed": 11,
     "status": "ok",
     "timestamp": 1686345632379,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "Vez-3UgFzglc",
    "outputId": "3f86d528-3e35-42be-8af4-448a8db6a3e0"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_53cbe_row0_col0, #T_53cbe_row2_col1 {\n",
       "  text-align: center;\n",
       "  border: 1px solid black !important;\n",
       "  background-color: #24479d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_53cbe_row0_col1 {\n",
       "  text-align: center;\n",
       "  border: 1px solid black !important;\n",
       "  background-color: #081d58;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_53cbe_row0_col2 {\n",
       "  text-align: center;\n",
       "  border: 1px solid black !important;\n",
       "  background-color: #1f78b4;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_53cbe_row0_col3 {\n",
       "  text-align: center;\n",
       "  border: 1px solid black !important;\n",
       "  background-color: #cdebb4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_53cbe_row1_col0 {\n",
       "  text-align: center;\n",
       "  border: 1px solid black !important;\n",
       "  background-color: #1e83ba;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_53cbe_row1_col1 {\n",
       "  text-align: center;\n",
       "  border: 1px solid black !important;\n",
       "  background-color: #243d98;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_53cbe_row1_col2 {\n",
       "  text-align: center;\n",
       "  border: 1px solid black !important;\n",
       "  background-color: #3cb1c3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_53cbe_row1_col3 {\n",
       "  text-align: center;\n",
       "  border: 1px solid black !important;\n",
       "  background-color: #feffd8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_53cbe_row2_col0 {\n",
       "  text-align: center;\n",
       "  border: 1px solid black !important;\n",
       "  background-color: #1f7bb6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_53cbe_row2_col2 {\n",
       "  text-align: center;\n",
       "  border: 1px solid black !important;\n",
       "  background-color: #34a9c3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_53cbe_row2_col3, #T_53cbe_row3_col3 {\n",
       "  text-align: center;\n",
       "  border: 1px solid black !important;\n",
       "  background-color: #ffffd9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_53cbe_row3_col0 {\n",
       "  text-align: center;\n",
       "  border: 1px solid black !important;\n",
       "  background-color: #1f80b8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_53cbe_row3_col1 {\n",
       "  text-align: center;\n",
       "  border: 1px solid black !important;\n",
       "  background-color: #24419a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_53cbe_row3_col2 {\n",
       "  text-align: center;\n",
       "  border: 1px solid black !important;\n",
       "  background-color: #39adc3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_53cbe\" class=\"dataframe\">\n",
       "  <caption>Four Model Performance Metrics</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_53cbe_level0_col0\" class=\"col_heading level0 col0\" >Decision Tree</th>\n",
       "      <th id=\"T_53cbe_level0_col1\" class=\"col_heading level0 col1\" >K-Nearest Neighbors</th>\n",
       "      <th id=\"T_53cbe_level0_col2\" class=\"col_heading level0 col2\" >Naive Bayes</th>\n",
       "      <th id=\"T_53cbe_level0_col3\" class=\"col_heading level0 col3\" >Artificial Neural Network</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_53cbe_level0_row0\" class=\"row_heading level0 row0\" >Accuracy</th>\n",
       "      <td id=\"T_53cbe_row0_col0\" class=\"data row0 col0\" >0.727273</td>\n",
       "      <td id=\"T_53cbe_row0_col1\" class=\"data row0 col1\" >0.753247</td>\n",
       "      <td id=\"T_53cbe_row0_col2\" class=\"data row0 col2\" >0.707792</td>\n",
       "      <td id=\"T_53cbe_row0_col3\" class=\"data row0 col3\" >0.642857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_53cbe_level0_row1\" class=\"row_heading level0 row1\" >Precision</th>\n",
       "      <td id=\"T_53cbe_row1_col0\" class=\"data row1 col0\" >0.703925</td>\n",
       "      <td id=\"T_53cbe_row1_col1\" class=\"data row1 col1\" >0.731179</td>\n",
       "      <td id=\"T_53cbe_row1_col2\" class=\"data row1 col2\" >0.683728</td>\n",
       "      <td id=\"T_53cbe_row1_col3\" class=\"data row1 col3\" >0.610000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_53cbe_level0_row2\" class=\"row_heading level0 row2\" >Recall</th>\n",
       "      <td id=\"T_53cbe_row2_col0\" class=\"data row2 col0\" >0.707071</td>\n",
       "      <td id=\"T_53cbe_row2_col1\" class=\"data row2 col1\" >0.727273</td>\n",
       "      <td id=\"T_53cbe_row2_col2\" class=\"data row2 col2\" >0.687879</td>\n",
       "      <td id=\"T_53cbe_row2_col3\" class=\"data row2 col3\" >0.609091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_53cbe_level0_row3\" class=\"row_heading level0 row3\" >F1-score</th>\n",
       "      <td id=\"T_53cbe_row3_col0\" class=\"data row3 col0\" >0.705357</td>\n",
       "      <td id=\"T_53cbe_row3_col1\" class=\"data row3 col1\" >0.729074</td>\n",
       "      <td id=\"T_53cbe_row3_col2\" class=\"data row3 col2\" >0.685500</td>\n",
       "      <td id=\"T_53cbe_row3_col3\" class=\"data row3 col3\" >0.609515</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f87bce634f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DT_result = metrics_calculator(y_test, DT_pred, 'Decision Tree')\n",
    "KNN_result = metrics_calculator(y_test, KNN_pred, 'K-Nearest Neighbors')\n",
    "NB_result = metrics_calculator(y_test, NB_pred, 'Naive Bayes')\n",
    "ANN_result = metrics_calculator(y_test, ANN_pred, 'Artificial Neural Network')\n",
    "\n",
    "# Convert styled DataFrames to regular DataFrames\n",
    "decision_tree_result_df = DT_result.data\n",
    "knn_result_df = KNN_result.data\n",
    "naive_bayes_result_df = NB_result.data\n",
    "artificial_neural_network_result_df = ANN_result.data\n",
    "\n",
    "Four_models = pd.concat([decision_tree_result_df, knn_result_df, naive_bayes_result_df, artificial_neural_network_result_df], axis=1)\n",
    "\n",
    "# Applying styling to the concatenated DataFrame\n",
    "styled_four = Four_models.style\\\n",
    "    .set_properties(**{'text-align': 'center', 'border': '1px solid black !important'})\\\n",
    "    .set_table_attributes('class=\"dataframe\"')\\\n",
    "    .set_caption('Four Model Performance Metrics')\\\n",
    "    .background_gradient(cmap='YlGnBu', vmin=Four_models.min().min(), vmax=Four_models.max().max())\n",
    "\n",
    "display(styled_four)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "X5LWGYVkETD3",
   "metadata": {
    "id": "X5LWGYVkETD3"
   },
   "source": [
    "# Confusion Matrix of Four Algorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "SaAYPQqwEfqW",
   "metadata": {
    "executionInfo": {
     "elapsed": 11,
     "status": "ok",
     "timestamp": 1686345632380,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "SaAYPQqwEfqW"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# Define a function to plot the confusion matrix\n",
    "def plot_confusion_matrix(cm, title):\n",
    "    ax = sns.heatmap(cm, annot=True, cmap='Blues', fmt='g')\n",
    "    ax.set_xlabel('Predicted labels')\n",
    "    ax.set_ylabel('True labels')\n",
    "    ax.set_title(title)\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# Generate the confusion matrix\n",
    "DTC_cm = confusion_matrix(y_test, DT_pred)\n",
    "KNN_cm = confusion_matrix(y_test, KNN_pred)\n",
    "NB_cm = confusion_matrix(y_test, NB_pred)\n",
    "ANN_cm = confusion_matrix(y_test, ANN_pred)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ZjN1BJRfE4yH",
   "metadata": {
    "id": "ZjN1BJRfE4yH"
   },
   "source": [
    "### Plot the confusion matrix for each classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "z6g2j-giEr92",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 480
    },
    "executionInfo": {
     "elapsed": 11,
     "status": "ok",
     "timestamp": 1686345632381,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "z6g2j-giEr92",
    "outputId": "423f216f-95b1-404f-90c9-75d3bfd5b3a9"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(DTC_cm, \"Decision Tree Classifier Confusion Matrix\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "zSLscQyGEu-V",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 480
    },
    "executionInfo": {
     "elapsed": 476,
     "status": "ok",
     "timestamp": 1686345632847,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "zSLscQyGEu-V",
    "outputId": "6c909b93-3381-4911-99da-4b713b7b4580"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(KNN_cm, \"K-Nearest Neighbours Classifier Confusion Matrix\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "89B1FxmfExTG",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 480
    },
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1686345632848,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "89B1FxmfExTG",
    "outputId": "9c593553-cc5b-4e50-cc48-6392ea7646ea"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(NB_cm, \"Naive Bayes Algorithm Classifier Confusion Matrix\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "W9DaUoYCE0-u",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 480
    },
    "executionInfo": {
     "elapsed": 550,
     "status": "ok",
     "timestamp": 1686345633389,
     "user": {
      "displayName": "Zamil Zamil",
      "userId": "04811903815352297421"
     },
     "user_tz": -360
    },
    "id": "W9DaUoYCE0-u",
    "outputId": "177a43c6-2953-411f-f33c-4de5c89a0fac"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiAAAAHPCAYAAABqc2HWAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABZJklEQVR4nO3dd1gU1/s28HtBEJEm1iigAtlVkWbDjiKKCNYYSxTsLVGj+ZqIJRiNiSUmKsWGioqKmtiVYI0lRrFExRoLNjBBRKU3Yd4/fNmf6wKy67KD7v3JtVfcOTNnntnGs885MysRBEEAERERkRbpiR0AERER6R4mIERERKR1TECIiIhI65iAEBERkdYxASEiIiKtYwJCREREWscEhIiIiLSOCQgRERFpHRMQIiIi0jomICUICAiAh4eHwrKMjAzMmDEDbdq0gUwmww8//ID4+HjIZDLs2LFDpf537NgBmUyG+Ph4jcT2IQsODoZMJhM7jHLHw8MDY8aMKfP9xMTEQCaTISYmpsz3VRyZTIbg4GCFZbGxsRgwYABcXFwgk8lw48YNnXutFPUYaFJ5eO7LGw8PDwQEBIgdxnvvg0hANm3aBJlMhk8//VTlbRMTExEcHFzqN+3KlSuxc+dODBw4EAsXLkTPnj1V3qc2+fn5QSaTYezYsUpthYnTmjVrRIisbAQEBEAmk6F79+4o6lcGZDIZ5syZo1bfK1aswOHDh981xHLn0KFDGDlyJNzc3NC4cWO0bdsWX375JU6fPi12aCXKy8vDpEmT8OLFC0ybNg0LFy5E7dq1RYnlxo0bmDJlCtzd3dG4cWO0aNECQ4cOxfbt25Gfn19m+y1Pj0FZKPz86tKlS5Htp06dgkwmg0wmQ3R0tMr937lzB8HBwWp9CaR3V0HsADRh7969qFOnDmJjY/HgwQPUrVu31Ns+efIEISEhqFOnDho2bKjQ9v333yv9ETtz5gycnZ0xfvx4+TJBEBAbG4sKFVR7OHv27AkfHx8YGhqqtJ06/vjjD1y9ehWNGzcu832VB7du3cLBgwfh5eWlsT5XrlwJLy8veHp6aqxPMQmCgOnTp2PHjh1o1KgRhg0bhmrVqiEpKQmHDh3C0KFDERkZiSZNmogdKoBX3/T19fXl9x8+fIiEhATMnTtX4cvHuHHjMHr0aK3F9euvv2LWrFmoWrUqevbsibp16yIjIwNnzpzBjBkzkJSUVOQXAE0o7jHQpObNmyM2NhYGBgZl0v/bVKxYEQ8ePEBsbCycnJwU2vbu3YuKFSsiJydHrb7v3LmDkJAQtGjRAlZWVqXeLjo6GhKJRK190v957xOQR48e4eLFiwgJCUFgYCD27t2rkBwU5+XLlygoKChxnaLecMnJybC3t1dYJpFIULFiRdUCB6Cvr6/wgVpWateujYyMDISEhGDFihVlvr/SyMzMhLGxcZn0bWRkhFq1aiE0NBRdunT5ID8osrKyUKlSpXfqY+3atdixYweGDBmCadOmKTxO48aNw65du1ROqsvSm++xZ8+eAQBMTU0VlleoUEGjcZf0WF+6dAmzZs2Ci4sLVq1aBRMTE3nb0KFDceXKFdy+fVtjsbypuMdAk/T09NT6fNMUGxsbvHz5Evv27VNIQHJycnDo0CF06NABBw4cKPM4BEFATk4OjIyMtPKlURe890Mwe/fuhbm5Odzd3eHl5YW9e/cqrfP6UMO6devg6ekJR0dHbN68GX379gUATJs2TV7KK5zL8fo8i8Jx0Pj4eBw7dky+bnx8fLFzQO7evYsvv/wSLVu2hJOTE7y8vLB48WJ5e1FzQA4fPozRo0ejbdu2aNy4MTw9PREaGvpOZdzKlStjyJAh+OOPP3Dt2rW3rp+amooffvhBXk7u3LkzVq1apZCwFTcuXNRjERAQAFdXVzx8+BCjRo2Cq6srpkyZAgA4f/48Jk6ciA4dOqBx48Zwd3fHjz/+iOzsbLWPV09PD+PGjcM///yDQ4cOvXX93NxcBAUFoXPnzvIYFi5ciNzcXPk6MpkMmZmZ2Llzp/y5DwgIwM2bNyGTyXDkyBH5ulevXoVMJkPv3r0V9jNy5Eilb6mbNm2Cj4+PfOhj9uzZSE1NVVjHz88Pvr6+uHr1KgYNGgRnZ2f88ssvxR7Pzp070ahRIyxYsKDYdbKzs7Fq1SrY2tpi6tSpRSZpvXr1UvrG+brSPndJSUmYNm0a2rdvLz/OcePGKbzur1y5ghEjRsDNzQ1OTk7w8PDAtGnTFPp5fQ5IQEAABg8eDAD48ssvIZPJ4OfnB6D4+UK7d+9Gnz594OTkhBYtWmDy5Mn4999/FdZR9bEOCQmBRCLBokWLFJKPQo6OjujTp4/8fmZmJubPny9/b3l5eWHNmjVKldbCocLDhw/D19cXjRs3ho+PD06cOCFfp6THwM/PT/7v1xU1d2z//v3o06cPXF1d0aRJE3Tv3h3r16+Xtxf3Xv/999/lj6ebmxumTJmCxMREpf25uroiMTERn3/+OVxdXdGyZUssWLBApc80X19fREVFKXwGHT16FNnZ2ejatavS+gkJCfjuu+/g5eUlj2/ixIkKr7kdO3bgyy+/BAD4+/vL39eFx1k4v+rkyZPy49yyZYu8rXAOiCAI8PPzQ8uWLZGcnCzvPzc3F927d4enpycyMzNLfay6pPx8vVHT3r170blzZxgaGsLX1xeRkZFFluqAVy+4nJwc9OvXD4aGhujcuTMyMjIQFBSE/v37o2nTpgBQZMnZzs4OCxcuxLx581CrVi0MGzYMAGBpaSn/FvK6mzdvYtCgQahQoQL69++POnXq4OHDhzh69CgmT55c7PHs3LkTxsbGGDZsGIyNjXHmzBkEBQUhPT0dU6dOVfdhwpAhQ7B+/XoEBweXWAXJysrC4MGDkZiYiAEDBuCjjz7CxYsX8csvvyApKQkzZsxQa/8vX77EiBEj0LRpU0ydOhVGRkYAXpUys7OzMXDgQFhYWCA2NhYbN27Ef//9h6CgILX2BQDdu3fH8uXLERoais6dOxdbBSkoKMC4ceNw4cIF9OvXD3Z2drh16xbWr1+P+/fvY9myZQCAhQsXYubMmXByckK/fv0AvPpmJpVKYWZmhvPnz6NTp04AXv1h1tPTw82bN5Geng4TExMUFBTg4sWL8m2BV38oQ0JC0Lp1awwcOBD37t1DZGQkrly5gsjISIUK3IsXLzBq1Cj4+PigR48eqFq1apHHs3XrVsyaNQtjxowp8XV24cIFvHjxAv7+/mpX4Ur73E2YMAF37tzB4MGDUadOHTx79gynTp3Cv//+CysrKyQnJ2PEiBGoUqUKRo8eDTMzM8THx5eYPPbv3x81a9bEihUr4OfnB0dHR1SrVq3Y9ZcvX46lS5fC29sbffv2xbNnz7Bx40YMGjQIu3btgpmZmXzd0j7WWVlZOHPmDJo1a1aqeReCIGDcuHGIiYlB37590bBhQ5w8eRILFy5EYmIipk+frrD+hQsXcPDgQXz22WeoXLkyIiIiMHHiRPzxxx+oUqWKyo9BUU6dOoWvvvoKrVq1kn8piIuLw99//40hQ4YUu92OHTswbdo0ODo64quvvkJycjI2bNiAv//+W+nxzM/Px4gRI+Dk5IRvvvkGp0+fxtq1a2FtbY3PPvusVHH6+voiODgYMTExaNWqFQBg3759aNmyZZHPz5UrV3Dx4kX4+PigVq1aSEhIQGRkJPz9/bF//35UqlQJzZs3h5+fHyIiIjB27FjY2toCePVZX+jevXv43//+h/79+6Nfv36oX7++0r4kEgl+/PFH9OjRA7NmzUJISAiAV+/v27dvIyIiosyqve894T125coVQSqVCqdOnRIEQRAKCgqE9u3bC3PnzlVY79GjR4JUKhWaNGkiJCcnK7TFxsYKUqlU2L59u1L/U6dOFTp27KiwrGPHjsLo0aOL7P/1PgYNGiS4uroKCQkJCusWFBTI/719+3ZBKpUKjx49ki/LyspSiuPbb78VnJ2dhZycnBJjK8rgwYMFHx8fQRAEITg4WJBKpcLVq1cV4l69erV8/dDQUMHFxUW4d++eQj+LFi0SGjZsKDx+/FgQBEE4c+aMIJVKhTNnzrz1sZg6daoglUqFRYsWKcVX1PGuXLlSkMlkCo9dUFCQIJVK33q8U6dOFVxcXARBEISdO3cKUqlUOHjwoLxdKpUKs2fPlt/ftWuX0KBBA+HcuXMK/URGRgpSqVS4cOGCfJmLi4swdepUpX2OHj1a6Nu3r/z++PHjhfHjxwsNGzYUjh8/LgiCIFy7dk2QSqXC4cOHBUEQhOTkZMHBwUEYPny4kJ+fL99248aNglQqFX777Tf5ssGDBwtSqVSIjIxU2vfrr8f169cLMplMCA0NfevjtH79ekEqlQqHDh1667qCUPTzXZrnLiUlRek19qZDhw4JUqlUiI2NLTEGqVQqBAUFKcX0+++/K6z35mslPj5eaNiwobB8+XKF9f755x+hUaNGCstLeqzfdOPGDUEqlSp93hSn8DiXLVumsHzChAmCTCYTHjx4oHCsDg4OCssK9xcRESFfVtxjMHjwYGHw4MFKMbz5uTF37lyhSZMmwsuXL4uN+83nPjc3V2jVqpXg6+srZGdny9f7448/BKlUKixdulRhf1KpVAgJCVHos1evXkLv3r2L3efrx1H4+dWnTx9h+vTpgiC8el05ODgIO3fuLPIxKOq1efHiRUEqlQo7d+6UL/v999+L/BwThFfvLalUKpw4caLItjc/C7Zs2SJIpVJh9+7dwqVLl4SGDRsKP/zww1uPUZe910Mwe/fuRbVq1eDm5gbgVSbarVs3REVFFVne69KlCywtLcs8rmfPnuHcuXP45JNPlL4ZvW0+QmFlAADS09Px7NkzNGvWDFlZWYiLi3unuIYMGQJzc3N5hl6U6OhoNG3aFGZmZnj27Jn81rp1a+Tn5+PcuXNq73/gwIFKy14/3szMTDx79gyurq4QBAHXr19Xe1/AqypIvXr1EBoaWuQZMcCr47Wzs4Otra3C8bZs2RIASnXqYdOmTXH9+nV5mfXChQto3749GjRogAsXLgB4VRWRSCTyKttff/2FvLw8+Pv7Q0/v/96Gn376KUxMTHD8+HGFfRgaGiqU8t8UFhaGH374AVOmTMHnn3/+1pjT09MBvBqeU1dpnjsjIyMYGBjg7NmzSElJKbKfwvkLx44dQ15entrxFOfQoUMoKCiAt7e3wnNcrVo11K1bV+k5fttjXUjVx/DEiRPQ19dXGhoZPnw4BEFQGF4BgNatW8PGxkZ+v0GDBjAxMcGjR49Ktb/SMDMzQ1ZWFk6dOlXqba5evYrk5GQMHDhQYW5Ihw4dYGtri2PHjilt8+Z7v2nTpiqfedK9e3ccOnQIubm5OHDgAPT19YudEP76azMvLw/Pnz+HjY0NzMzMVPpcsbKyQrt27Uq1bv/+/dG2bVvMnTsX33zzDaytrfHVV1+Vel+66L0dgsnPz8f+/fvh5uam8EJ2cnLC2rVrcfr0abRt21ZhG1VmOb+Lwg8IqVSq8ra3b9/GkiVLcObMGfkHXKG0tLR3isvU1BT+/v4IDg7G9evXFcqkhR48eIB//vlHXuZ8U1HDTaVRoUIF1KpVS2n548ePERQUhKNHjyr9gXrz+FWlr6+PcePGYerUqTh8+DA6d+6stM6DBw9w9+7dYo/39THd4jRr1gwvX77EpUuXUKtWLSQnJ6NZs2a4c+cOzp8/D+BVAmJvbw8LCwsAr44bgLzsW8jQ0BDW1tZISEhQWF6zZs1iJ76dPXsWx44dw6hRozBy5Mi3xgtAPl8hIyOjVOsXpTTPnaGhIaZMmYIFCxagTZs2cHZ2RocOHdCrVy9Ur14dANCiRQt4eXkhJCQE69atQ4sWLeDp6Ynu3btrZLLf/fv3IQhCsadyvjlhtaTH+nWqPoYJCQmoUaOG0lyRwpL/m8/5Rx99pNSHubm50hyhd/HZZ5/h999/x6hRo1CzZk20adMG3t7eaN++fbHbFL52ixqOsLW1lSfdhSpWrKj0xc/c3LzYhLQ43bp1w4IFC3DixAns2bMHHTp0KHLeDfBqjtPKlSuxY8cOJCYmKnwBUeVzVNW/GT/++CM8PT2RkpKCLVu2KCRCpOy9TUDOnDmDpKQk7N+/H/v371dq37t3r1ICUt5fDKmpqRg8eDBMTEwwceJE2NjYoGLFirh27RoWLVr01rN2SqNwLkhISIjSmDPwak5EmzZtiv1DVq9ePQDFV3KKi9HQ0FDhmz7wKokcNmwYUlJSMHLkSNja2sLY2BiJiYkICAjQyPF2794dy5YtQ2hoaJHflgoKCiCVSpUmPBYqKml6U+PGjVGxYkWcO3cOtWvXRtWqVVG/fn00a9YMmzdvRm5uLi5cuPBOp++W9Nr9+OOPkZqait27d6N///6wtrZ+a3+Fic8///yjVlyqPHdDhw6Fh4cHDh8+jD///BNLly7FqlWrsH79ejRq1AgSiQRBQUG4dOkS/vjjD5w8eRLTp09HeHg4tm7d+k5VGuDVcyyRSBAWFlbkfJc3x+dL+zlRt25dVKhQAbdu3Xqn+IpT3Nyc4qp5pfFmZbhq1arYtWsX/vzzT5w4cQInTpzAjh070KtXrxInMatCU2f61ahRAy1atEB4eDj+/vtvpYvSve7777+Xn+Hl4uICU1NTSCQSTJ48WaXHT9W/GTExMfLJ67du3YKrq6tK2+ua9zYB2bt3L6pWrYrAwECltkOHDuHQoUOYPXv2W19AZXGKZuEfAFU/mM6ePYsXL14gJCQEzZs3ly/X5EVyTE1NMWTIEAQHByudpQG8mliZmZmJ1q1bl9hPYfXkzW8Tb36LK8mtW7dw//59LFiwAL169ZIvV6Uc/DaFVZCAgACFM1UK2djY4ObNm2jVqpXarwVDQ0M4OTnh/PnzqF27Npo1awbgVZk5NzcXe/bswdOnTxWe08Khubi4OIWEITc3F/Hx8W99/F9XpUoVBAUF4bPPPsPQoUOxefNm1KxZs8RtmjZtCnNzc+zfvx9jx45V+Y+Eqs+djY0Nhg8fjuHDh+P+/fvo1asX1q5di0WLFsnXcXFxgYuLCyZPnoy9e/diypQpiIqKeufrW9jY2EAQBFhZWRX5rV1dlSpVQsuWLXHmzBn8+++/RVYsXlenTh2cPn1aPjG5UOHQap06dTQWm7m5eZFDNYXVi9cZGhrCw8MDHh4eKCgowHfffYetW7fi888/L/KaSoWv3Xv37ilVDu/du1emF0Lz9fXFzJkzYWZmVmKV5sCBA+jVq5fC1UpzcnKUPq80+fn/5MkTzJ07F23btoWBgQEWLFiAtm3bavR5/dC8l3NAsrOzcfDgQXTo0AFdu3ZVug0aNAgZGRk4evToW/sqPL9fk2VNS0tLNG/eHNu3b1d6w5eUfRdWCF5fJzc3F5s3b9ZYbMCrKoiZmRlCQ0OV2ry9vXHx4kWcPHlSqS01NRUvX74E8OrDUl9fX2lOSGRkZKnjKOp4BUHAhg0bSt1HafTo0QN169Ytcu6Lt7c3EhMTsW3bNqW27OxshdPnjI2Ni32dNG3aFLGxsYiJiZHP87C0tISdnR3CwsIAQJ6YAK/G9w0MDBAREaFw/L/99hvS0tLg7u6u0jHWqlUL4eHhyMnJwfDhw/H8+fMS169UqRJGjhyJu3fvYtGiRUW+Lnfv3o3Y2Ngity/tc5eVlaV0kSgbGxtUrlxZ/k0xJSVFaf+FFwV8/VRodXXp0gX6+voICQlR2o8gCG99rEryxRdfQBAEfPPNN0UOxVy9ehU7d+4EALRv3x75+fnYtGmTwjrr1q2DRCIp8Q+qqqytrREXF6cwZHrz5k38/fffCuu9eex6enryU5iLe+wbN26MqlWrYsuWLQrrHD9+HHfv3kWHDh00dBTKunbtivHjx2PWrFklDpMVlVBHREQoVYAKP//fdXgbAL799lsUFBTghx9+wJw5c1ChQgXMmDHjnSpWH7r3sgJy9OhRZGRkFPtbKC4uLrC0tMSePXvQrVu3EvsqnJi0ZcsWVK5cGcbGxnBycipVGbskM2fOxMCBA9G7d2/0798fVlZWSEhIwLFjx7B79+4it3F1dYW5uTkCAgLg5+cHiUSC3bt3a/wFXDgXpKg/yCNGjMDRo0cxduxY9O7dGw4ODsjKysKtW7dw4MABHDlyBJaWljA1NUXXrl2xceNGSCQSWFtb49ixY6WaM1HI1tYWNjY2WLBgARITE2FiYoIDBw5oNBkEXn0YjR07tshhlp49e+L333/HrFmzEBMTgyZNmiA/Px9xcXGIjo7G6tWr4ejoCABwcHDA6dOnER4ejho1asDKygrOzs4AXiUXK1aswL///quQaDRr1gxbt25FnTp1FIZzLC0tMWbMGISEhGDkyJHw8PDAvXv3sHnzZjg6OqJHjx4qH2fdunWxZs0a+Pv7Y8SIEdiwYUOxY+TAq+uS3LlzB2vXrkVMTAy8vLxQrVo1PH36FIcPH0ZsbKz8ugdvKu1zd//+fQwdOhRdu3aFvb099PX1cfjwYTx9+hQ+Pj4AXp16HhkZCU9PT9jY2CAjIwPbtm2DiYmJRv4o29jYYNKkSfj555+RkJAAT09PVK5cGfHx8Th8+DD69euHESNGqNV3kyZNEBgYiNmzZ8Pb21vhSqhnz57F0aNHMWnSJACvrh3h5uaGxYsXIyEhATKZDKdOncKRI0cwZMgQhQmn76pv375Yt24dRowYgb59+yI5ORlbtmyBvb29QqI0c+ZMpKSkoGXLlqhZsyYeP36MjRs3omHDhgqno77OwMAAU6ZMwbRp0zB48GD4+PjIT8OtU6cOhg4dqrHjeJOpqSkmTJjw1vU6dOiA3bt3w8TEBPb29rh06RL++usv+RysQg0bNoS+vj7CwsKQlpYGQ0PDYk/tLcn27dtx7NgxzJ8/X/4+nzlzJr7++mts3rwZgwYNUqk/XfFeJiB79uxBxYoV0aZNmyLb9fT00KFDB+zdu/et324MDAwwf/58/PLLL/juu+/w8uVLzJs3750TkAYNGmDbtm1YunQpIiMjkZOTg9q1a8Pb27vYbapUqYIVK1ZgwYIFWLJkCczMzNCjRw+0atVK7Q/I4hTOBXkz869UqRIiIiKwcuVKREdHY9euXTAxMUG9evUwYcIEhSsuzpw5Ey9fvsSWLVtgaGiIrl274ptvvoGvr2+pYjAwMMCKFSswd+5crFy5EhUrVkTnzp0xaNAgjf/GTo8ePbB8+XI8fPhQYbmenh5CQ0Oxbt067N69G4cOHUKlSpVgZWUFPz8/hZJ9QEAAAgMDsWTJEmRnZ6N3797yBMTV1RX6+vowMjJCgwYN5NsUJiCvJyWFJkyYAEtLS2zcuBHz5s2Dubk5+vXrh6+++krty17LZDKEhYVh6NChGDt2LFavXl3sMKSenh4WLlyITp06Ydu2bVi7di3S09NRpUoVNG/eHF9//XWxY9ilfe5q1aoFHx8fnD59Gnv27IG+vj5sbW2xZMkS+WXyW7RogStXriAqKgpPnz6FqakpnJycsGjRond+HxYaPXo06tWrh3Xr1skrf7Vq1UKbNm3e+UcdBwwYAEdHR6xduxa7du3C8+fPYWxsjEaNGmHevHnyZFJPTw/Lly9HUFAQoqKisGPHDtSpUwfffPMNhg8f/s7H+Do7OzssWLAAQUFBmDdvHuzt7bFw4ULs27cPZ8+ela/Xo0cPbNu2DZs3b0ZqaiqqV68Ob29vTJgwQWnO1uv69OkDIyMjhIWFYdGiRTA2Noanpye+/vrrIie3a9uMGTOgp6eHvXv3IicnB02aNEF4eLjS3Lbq1atj9uzZWLlyJWbMmIH8/Hxs2LBBpQTkv//+w7x589CxY0eFYe0ePXrg4MGDWLRoEdq3b6+x1/KHRCKwPkRERERa9l7OASEiIqL3GxMQIiIi0jomIERERKR1TECIiIhI65iAEBERkdYxASEiIiKtYwJCREREWvdeXohMEyq5jhc7BKJy5/k55avjEhFgVMZ/LTX5Nynr4vvxPtbZBISIiKjckOjegITuHTERERGJjhUQIiIisUkkYkegdUxAiIiIxKaDQzBMQIiIiMSmgxUQ3Uu5iIiISHSsgBAREYmNQzBERESkdRyCISIiIip7rIAQERGJTQeHYHTviImIiMobiURzNzX5+flBJpMVedu/f798vV9//RVeXl5wdHREjx498Mcff6i1P1ZAiIiICLNmzUJ6errCsvXr1+PgwYNo1aoVAGD//v349ttvMXbsWLRs2RJRUVEYP348Nm3aBBcXF5X2xwSEiIhIbOVgCMbe3l5p2f/+9z+0adMGlpaWAICgoCD4+Phg0qRJAICWLVvi1q1bCA0NRVhYmEr7E/+IiYiIdF05GIJ5099//434+Hh0794dAPDo0SPcv38f3t7eCut169YNp0+fRm5urkr9swJCRET0AenUqVOJ7UeOHClVP/v27YOxsbG8v7i4OABA/fr1Fdazs7NDXl4eHj16BDs7u1LHyQSEiIhIbOVgCOZ1L1++xO+//w4PDw8YGxsDAFJSUgAAZmZmCusW3i9sLy0mIERERGLT4NBJaSscJTl16hSePXsGX19fDURUtPKVchEREekiiZ7mbhqwb98+WFhYoG3btvJl5ubmAIC0tDSFdVNTUxXaS4sJCBEREcllZ2fj8OHD6Nq1KwwMDOTLbW1tAfzfXJBCcXFxMDAwgLW1tUr7YQJCREQktnJUATl69CgyMzPlZ78Usra2Rr169RAdHa2wPCoqCq1atYKhoaFK++EcECIiIrHplZ8fo9u7dy9q166Npk2bKrVNmDABU6ZMgY2NDdzc3BAVFYXY2Fhs3LhR5f0wASEiIiIAr85kOXnyJIYMGQJJERNjfX19kZWVhbCwMKxatQr169dHSEgIXF1dVd4XExAiIiKxlZPTcM3NzXH16tUS1/n000/x6aefvvO+mIAQERGJTYOn4b4vykfKRURERDqFFRAiIiKxlZMhGG1iAkJERCQ2DsEQERERlT1WQIiIiMTGIRgiIiLSOh0cgmECQkREJDYdrIDo3hETERGR6FgBISIiEhuHYIiIiEjrOARDREREVPZYASEiIhIbh2CIiIhI6zgEQ0RERFT2WAEhIiISmw5WQJiAEBERiU0H54DoXspFREREomMFhIiISGwcgiEiIiKt08EhGCYgREREYtPBCojuHTERERGJjhUQIiIisXEIhoiIiLRNooMJCIdgiIiISOtYASEiIhKZLlZAmIAQERGJTffyDw7BEBERkfaxAkJERCSy8jQEs3PnTqxfvx53796FsbExHB0dERISAiMjIwQEBGDnzp1K24SFhaF9+/Yq7YcJCBERkcjKSwKyfPlyhIWFYezYsXBxccHz589x+vRp5Ofny9extrbGokWLFLazs7NTeV9MQIiIiAhxcXEICQnBsmXL4O7uLl/u5eWlsJ6RkRFcXFzeeX9MQIiIiERWHiogO3bsgJWVlULyUZaYgBAREYlMkwlIp06dSmw/cuRIkcsvX74MqVSKZcuWISIiAmlpaWjcuDGmTZsGZ2dn+XoPHjxA06ZNkZOTA6lUis8//xyenp4qx8kEhIiISGziF0CQlJSEq1ev4tatW5g1axYqVaqEFStWYPjw4Th48CCqVq2Khg0bwtHREfb29khLS0NkZCS++OILLF26FF27dlVpfxJBEIQyOpZyrZLreLFDICp3np8LETsEonLJqIy/rpt/FqGxvlI2+6m1nZeXF+7fv4/du3ejQYMGAIAXL17Aw8MDQ4YMwZdffqm0TUFBAQYMGID09HRERUWptD9eB4SIiEhkEolEYzd1mZmZwcLCQp58AICFhQUaNWqEO3fuFLmNnp4eunTpgrt37yI7O1ul/TEBISIiEll5SEDs7e2LbcvJyVG73+IwASEiIiJ07NgRL168wI0bN+TLnj9/jmvXrsHBwaHIbQoKChAdHY2PP/4YRkZGKu2Pk1CJiIhEVh5Ow/X09ISjoyMmTpyIyZMno2LFili1ahUMDQ3x2WefISEhAQEBAfDx8UHdunWRkpKCyMhIXL16FcHBwSrvjwkIERGRyMpDAqKnp4dVq1Zh3rx5CAwMRF5eHpo1a4ZNmzahevXqePHiBUxMTLB8+XIkJyfDwMAAjRs3RlhYGNq1a6fy/ngWDBHJ8SwYoqKV9VkwVf0jNdZX8oaBGuurLLECQkREJDbxCyBaxwSEiIhIZOVhCEbbeBYMERERaR0rIERERCLTxQoIExAiIiKRMQEhIiIi7dO9/INzQIiIiEj7WAEhIiISGYdgiIiISOt0MQHhEAwRERFpHSsgREREItPFCggTECIiIpHpYgLCIRgiIiLSOlZAiIiIxKZ7BRAmIERERGLjEAwRERGRFrACQkREJDJdrIAwASEiIhIZExAiIiLSPt3LPzgHhIiIiLSPFRAqU6tmD4Zfj5bFttt1mYHnaZnw79EKvh0c4WBfGybGFXH3URLW7jiFNdtPoaBA0GLERGXv6pVY7Nm9C+fOxuDx4wRYmFvAydkZX0ychHr16svXc3aQFdtHy1atsXJ1uDbCJS3gEAyRhq3ZfgpHY/5RWCaRAMEzBuDB42d4nJSCRnYf4ZepffHH2VsI3ngUqRnZ6NyqIYKmD0ALx/oYFRghUvREZSN8zWpcuvg3Ont1hVQqw9OnSdiyeRMG9O2DiMit+PhjKQDgh/kLlba9fvUqNm3cgFat22g7bCpDTECINCwm9h5iYu8pLGvtYovKlSpiS9Q5AEBiciqaffojbsT9J19nzfZTWDFrEIb0aoV5Yb8j7tFTrcZNVJb8hgzF/IWLYGBoKF/m5d0NfXt1x9rVqzBvwSIAgG/3nkrbnj93FhKJBN7dfLUWL1FZ4BwQ0rp+3s1QUFCArb+fBwAkv8hQSD4K7fnjMgCgQf1aWo2PqKy5uDZRSD4AoG7derCz/xj34uKK3S43NxeHDx1E02bNUbMW3xcfEolEorHb+4IJCGlVhQp6+KRzE5y5fA8P/31W4ro1q5oBeJWgEH3oBEFAcvJTWFhUKXadkyeOIy01FT6+PbQYGWkDExCiMta5VSNUq2KCLb+fK3E9gwr6GD+oI+7FP8X5aw+0FB2RePbv24MniYnw8vYudp2ofXthaGgIzy5eWoyMqGyUuzkgSUlJOHXqFOLi4vDixQsAgIWFBWxtbdGmTRtUr15d3ADpnfT3bobcvJfYfvBiiestDuiHRnYfodeEZcjPL9BSdETiuBd3F/PmzoGziyt69Oxd5Drp6ek4eeIY2rZ3h5mZmZYjpDL3/hQuNKbcJCB5eXlYsGABtmzZgvz8fFSvXh3m5uYAgJSUFCQlJUFfXx8DBgxAQEAAKlQoN6FTKVWuZAjfDo449NcNPEspflhlsn8njPikDb4L3YsDf17XYoRE2vc0KQnjPx8DExNTLFq8FPr6+kWud/jQAeTk5MDHp7uWIyRteJ+GTjSl3PwVX7JkCXbv3o3AwEB4e3vD1NRUoT09PR2///47fvrpJxgZGWHKlCkiRUrq6t7RGZUrVZRPPi3K4O5umPtlT6z69SQWrD6gxeiItC8tLQ2fjx2FtNQ0hG/YhBo1aha7btS+vTA1NUX7Dh21GCFpS3lKQHbu3In169fj7t27MDY2hqOjI0JCQmBkZAQAOHr0KJYsWYJ79+6hdu3aGD16ND755BOV91NuEpDdu3dj2rRp6NOnT5HtJiYm+PTTT6Gnp4fFixczAXkPDejWDGkZ2dh3PLbIdt8Ojlge+Bl2H72MSfO2aTk6Iu3KycnBxC/G4sGD+1i1Ohx29vbFrpuU9ATnzsagR6/eMHzj7BkiTVq+fDnCwsIwduxYuLi44Pnz5zh9+jTy8/MBAOfPn8f48ePRt29fTJ8+HWfOnMGMGTNQuXJldO3aVaV9lZsEJCMjA7VKcVpZrVq1kJHBsyLeN9WqmMCjRQNsO3AeWdl5Su1tmthhw7xh+PPvOxg6fT0EgVc/pQ9Xfn4+vvnfJMRevoQlwcvg7OJa4vrRUVEoKChANw6/fLDKQwEkLi4OISEhWLZsGdzd3eXLvbz+b9Lz8uXL4eTkhDlz5gAAWrZsiUePHiEoKOj9TUBcXFywYsUKODo6Kg2/FEpPT8eKFSvg6lrym5XKn75dmsDAQB9bopSHX2w+qoLfloyBIAA7D19Cn86Kz+/V2wm4evuxtkIlKnM/L5yPY38chXuHjkhJeYF9e3crtL95AbKo/XtQvUYNNG/hps0wSYvKwxDMjh07YGVlpZB8vC43NxcxMTFKIxDdunXDvn37EB8fDysrq1Lvr9wkIN9++y2GDBkCd3d3tG7dGra2tvJEJD09HXFxcfjrr79QuXJlrFu3TtxgSWUDujVHYnIqjsbcVGqrW7saLEyNAQBLp/dXap+7IooJCH1Q/vnn1fvg+LE/cPzYH0rtrycg9+/F4fq1a/AbMgx6erxyAr1dp06dSmw/cuRIkcsvX74MqVSKZcuWISIiAmlpaWjcuDGmTZsGZ2dnPHz4EHl5ebC1tVXYzs7ODsCrCsp7mYDY2tpi//79iIyMxMmTJ/Hbb78hNTUVAGBmZgZbW1uMGTMGAwYM4Clo76EOQ34utu3khduo5Dpei9EQiWvNutL/vlG9+ra4fO2ft69I77VyUABBUlISrl69ilu3bmHWrFmoVKkSVqxYgeHDh+PgwYNISUkBAKW/wYX3C9tLq9wkIMCrgxgzZgzGjBkjdihERERao8khmOIqHG8jCAIyMzOxdOlSNGjQAADg7OwMDw8PbNy4EW3bttVYjACvhEpERER4VQSwsLCQJx/AqwuBNmrUCHfu3JFfmystLU1hu8LRisL20mICQkREJDKJRHM3ddmXcCp4Tk4ObGxsYGBggLg3fjCx8P6bc0PehgkIERGRyPT0JBq7qatjx4548eIFbty4IV/2/PlzXLt2DQ4ODjA0NISbmxsOHFC8SGRUVBTs7OxUmoAKlLM5IERERCQOT09PODo6YuLEiZg8eTIqVqyIVatWwdDQEJ999hkAYNy4cfD398d3330Hb29vxMTEYN++fVi8eLHK+2MCQkREJLLycBaMnp4eVq1ahXnz5iEwMBB5eXlo1qwZNm3aJP8h2GbNmiE4OBhLlizBb7/9htq1a2Pu3LnwLuFXnIvDBISIiEhk5eFCZABgaWmJn376qcR1OnXq9NZrjZQGExAiIiKRlZP8Q6s4CZWIiIi0jhUQIiIikZWXIRhtYgJCREQkMl1MQDgEQ0RERFrHCggREZHIdLAAwgSEiIhIbByCISIiItICVkCIiIhEpoMFECYgREREYuMQDBEREZEWsAJCREQkMh0sgDABISIiEpsuDsEwASEiIhKZDuYfnANCRERE2scKCBERkcg4BENERERap4P5B4dgiIiISPtYASEiIhIZh2CIiIhI63Qw/+AQDBEREWkfKyBEREQi4xAMERERaZ0O5h8cgiEiIiLtYwWEiIhIZByCISIiIq1jAkJERERap4P5B+eAEBERkfaxAkJERCSy8jAEs2PHDkybNk1p+ahRozBlyhQAgJ+fH86ePau0TlRUFOzs7FTaHxMQIiIikZWD/ENu9erVMDU1ld+vWbOmQnuTJk0wdepUhWVWVlYq74cJCBEREck5ODjA0tKy2HYzMzO4uLi8836YgBAREYmsPAzBaBsTECIiIpFpMv/o1KlTie1Hjhwpsd3X1xfPnz9H7dq10a9fP4wcORL6+vry9rNnz8LFxQX5+flwdnbGl19+iebNm6scJxMQIiIiQvXq1TFhwgQ4OztDIpHg6NGjWLJkCRITExEYGAgAaN68OXr27Il69erhyZMnWLNmDYYNG4aIiAi4urqqtD+JIAhCWRxIeVfJdbzYIRCVO8/PhYgdAlG5ZFTGX9c7h5zRWF+HxrfUWF8LFizA+vXrcezYMdSoUUOpPTMzE76+vrCzs0NYWJhKffM6IERERCKTSDR30yRvb2/k5+fjxo0bRbYbGxvD3d0d165dU7lvtXK69PR0pKWl4aOPPpIvS0xMxJYtW5CbmwsvLy84OTmp0zURERHpALUSkMDAQMTHx2Pbtm0AXiUk/fv3x3///Qc9PT1s2LABq1evhpubm0aDJSIi+hCV17NgoqKioK+vj0aNGhXZnpmZiWPHjsHR0VHlvtVKQC5cuID+/fvL7+/evRtPnjzBli1bYG9vj6FDh2L58uVMQIiIiEpBrxzkHyNGjICbmxtkMhmAV2fLbNu2Df7+/qhevTrOnz+P1atXo3PnzqhTpw6ePHmC8PBwJCUlYenSpSrvT60E5Pnz5wpXRjt69CiaNm0qvzBJr169EBLCyWxERESlUR4qIPXr18f27dvx33//oaCgAPXq1cP06dPh5+cH4NVZMnl5eVi8eDFevHiBSpUqwdXVFbNnz1Zr2oVaCYiZmRmePn0KAMjOzsaFCxcwduxYebu+vj6ys7PV6ZqIiIhEMHPmzBLb69atizVr1mhsf2olIK6urti8eTNsbW1x8uRJ5OTkKFz45P79+0rXjiciIqKilYMCiNapdRrulClTUKFCBUyYMAHbtm3D0KFD8fHHHwMA8vPzER0drdZV0YiIiHSRRIP/vS/UqoDUrVsX0dHRuHv3LkxMTBR+BS8rKwvffvstGjRooLEgiYiI6MOi9rXdDAwMikwyTExM4Onp+U5BERER6ZLycBaMtpUqATl37pxanXMYhoiI6O3Kw1kw2laqBMTPz0+lB0cQBEgkkmIv3UpERES6rVQJyIYNG8o6DiIiIp2lgwWQ0iUgLVq0KOs4iIiIdJaeDmYg7/xruE+ePMHNmzeRmZmpiXiIiIhIB6idgBw+fBhdu3aFu7s7evfujcuXLwMAnj17hl69euHw4cMaC5KIiOhDJpFo7va+UCsBOXr0KCZMmIAqVargiy++gCAI8jZLS0vUrFkT27dv11iQREREHzKJRKKx2/tCrQQkNDQUzZo1Q2RkJAYNGqTU7uLiwjNgiIiISokVkFK6ffs2vL29i22vVq0akpOT1Q6KiIiIPmxqXQm1UqVKyMrKKrb90aNHsLCwUDcmIiIincKzYErJzc0Nu3btwsuXL5XakpKSsG3bNrRt2/adgyMiItIFEg3e3hdqJSCTJk3Cf//9h759+2Lr1q2QSCT4888/sXjxYnTv3h2CIOCLL77QdKxERET0gVArAbG1tcXmzZthYWGBpUuXQhAErFmzBitXroRUKsXmzZsVfiGXiIiIiqeLZ8Go/Wu4H3/8MdatW4eUlBQ8ePAAgiDA2toalpaWmoyPiIjog8dfw1WDubk5nJycNBELERER6Qi1E5Bnz54hLCwMx48fR0JCAgCgTp06cHd3x4gRI1CtWjWNBUlERPQhe5+GTjRF7euAdO/eHeHh4TA1NUXXrl3RtWtXmJqaIjw8HD169MCtW7c0HSsREdEHSRcvRKZWBWTOnDnIz8/Htm3blIZfYmNjMWrUKHz//feIiIjQSJBERET0YVGrAhIbGwt/f/8i5344OTnB398fsbGx7xwcERGRLuBZMKVUtWpVVKxYsdj2ihUromrVqmoHRUREpEt08SwYtSog/v7+iIyMRFJSklJbYmIiIiMj4e/v/87BERER6QJWQIoRHh6utMzY2BhdunSBp6cn6tatCwC4f/8+jhw5AhsbG81GSURERB8UiSAIwttWatCggeodSyS4ceOGWkFpQyXX8WKHQFTuPD8XInYIROWS0TtfNatkw7dc0Vhfawc4aqyvslSqh/TIkSNlHQcREZHO0sVfwy1VAlKnTp2yjoOIiIhEtGPHDkybNk1p+ahRozBlyhT5/V9//RWrV6/G48ePUb9+fUyePBkdO3ZUeX9lXFQiIiKitylPBZDVq1fD1NRUfr9mzZryf+/fvx/ffvstxo4di5YtWyIqKgrjx4/Hpk2b4OLiotJ+1E5Abt68iY0bN+L69etIS0tDQUGBQrtEIsHhw4fV7Z6IiEhnlKezVxwcHIr9YdmgoCD4+Phg0qRJAICWLVvi1q1bCA0NRVhYmEr7Ues03JiYGHz66ac4duwYatSogUePHsHa2ho1atTA48ePYWxsjObNm6vTNREREZVDjx49wv379+Ht7a2wvFu3bjh9+jRyc3NV6k+tCkhQUBCsra2xbds25ObmonXr1hgzZgxatWqFy5cvK40XERERUfE0WQDp1KlTie1vO7HE19cXz58/R+3atdGvXz+MHDkS+vr6iIuLAwDUr19fYX07Ozvk5eXh0aNHsLOzK3WcaiUg169fx4QJE2BiYoKUlBQAkA/BODs7o3///li6dCnc3d3V6Z6IiEinlIezYKpXr44JEybA2dkZEokER48exZIlS5CYmIjAwED533szMzOF7QrvF7aXlloJiL6+PipXrizfcYUKFZCcnCxvt7a2xt27d9XpmoiIiN6BupfOaNeuHdq1aye/37ZtW1SsWBHr16/H2LFjNRWenFpzQGxsbHD//n0ArybO2NraKkw4PXbsGKpVq6aRAImIiD50Eonmbprk7e2N/Px83LhxA+bm5gCAtLQ0hXVSU1MBQN5eWmolIO7u7ti/fz9evnwJABg2bBgOHjyILl26oEuXLjh69Cj69++vTtdEREQ65334LRhbW1sAkM8FKRQXFwcDAwNYW1ur1J9aQzCff/45/P39oa+vDwDo3bs39PT0cPDgQejr62Ps2LHo06ePOl1rTdKZYLFDICIiAqBmNUALoqKioK+vj0aNGqF69eqoV68eoqOj4enpqbBOq1atYGhoqFLfaiUgBgYGqFKlisKynj17omfPnup0R0RERCIbMWIE3NzcIJPJALyaS7Jt2zb4+/ujevXqAIAJEyZgypQpsLGxgZubG6KiohAbG4uNGzeqvD9eCZWIiEhk5eFCZPXr18f27dvx33//oaCgAPXq1cP06dPh5+cnX8fX1xdZWVkICwvDqlWrUL9+fYSEhMDV1VXl/ZXq13D9/f1V71giwfr161XeTlvSc9562EQ6p4K++B+CROVRWf8a7qTdNzXW15Keqv+CvRhK9ZCWIkfRyDZERESkG0qVgERERJR1HERERDpLTweLj5wDQkREJLLyMAdE28rrmT9ERET0AWMFhIiISGQcgiEiIiKt08ERGA7BEBERkfaxAkJERCQyPR0sgbxTApKYmIhz584hOTkZXl5eqFWrFvLz85GWlgZTU1P5b8UQERFR8XRxOEKtBEQQBMyfPx+bNm3Cy5cvIZFIIJVKUatWLWRmZsLDwwMTJ07E0KFDNRwuERHRh0cHCyDqJV2rV6/Ghg0bMHz4cISHhytc9dTU1BRdunTBwYMHNRYkERERfVjUqoD8+uuv6NWrF7766is8f/5cqV0mk+HEiRPvHBwREZEu4ByQUvr3339L/OW7SpUqIT09Xe2giIiIdIkO5h/qDcFUrVoV//77b7Ht165dw0cffaR2UERERPRhUysB6dy5M7Zs2YJHjx7JlxVex/7PP//Ezp070bVrV81ESERE9IHTk2ju9r6QCK/PIC2ltLQ0DBo0CPHx8WjWrBlOnjyJ1q1bIzMzE5cuXULDhg2xadMmVKpUqSxi1oj0HJUPm+iDV0H/Pfr0ItIiozK+atacQ3c01ldgZ3uN9VWW1KqAmJqaYtu2bRg5ciQSExNRsWJFnDt3Dmlpafjiiy+wefPmcp18EBERkbjUqoB8CFgBIVLGCghR0cq6AvL9Yc1VQL71fD8qILwUOxERkcjep7kbmqJWAjJt2rS3riORSPDjjz+q0z0RERF94NRKQGJiYpSWFRQUICkpCfn5+bC0tOQcECIiolKSQPdKIGolIEePHi1yeV5eHrZu3Yr169dj7dq17xQYERGRrtDFIRiN/gCfgYEBBg8ejDZt2uD777/XZNdEREQfLF28DkiZ/AJwgwYNcO7cubLomoiIiD4AZXIWzF9//cU5IERERKUk0cEfg1ErAQkJCSlyeVpaGs6dO4fr169j9OjR7xQYERGRrnifhk40RaMJiLm5OaytrTF79mz069fvnQIjIiKiD5daCcjNmzc1HQcREZHO0sERGNUnoWZnZ2PevHnFnopLREREqtGTSDR205SMjAy0b98eMpkMV65ckS/38/ODTCZTut29e1el/lWugBgZGWHr1q2wt38/rjVPREREqlu2bBny8/OLbGvSpAmmTp2qsMzKykql/tUagnFwcMCtW7fU2ZSIiIjeUN4mod69exebN2/G1KlTMWvWLKV2MzMzuLi4vNM+1LoOyPTp0xEVFYVff/0VL1++fKcAiIiIdJ1EormbJsydOxcDBgxA/fr1NdNhEUpdATl37hzs7OxgaWmJgIAASCQSBAYGYu7cuahZsyYqVqyosL5EIsGePXs0HjAREREVr1OnTiW2HzlypMT26Oho3Lp1C8HBwbh27VqR65w9exYuLi7Iz8+Hs7MzvvzySzRv3lylOEudgPj7++Onn36Cr68vLCwsYGFhUaaZERERka7QKyc/RpeVlYX58+dj8uTJMDExKXKd5s2bo2fPnqhXrx6ePHmCNWvWYNiwYYiIiICrq2up91XqBEQQBAiCAACIiIgo9Q6IiIioZJo8DfdtFY6SLF++HFWrVsUnn3xS7DoTJ05UuN+hQwf4+vpi2bJlCAsLK/W+yuS3YIiIiKj0ysOP0SUkJGDt2rWYOHEi0tLSkJqaiszMTABAZmYmMjIyitzO2NgY7u7uxQ7XFEels2B08Vr1REREuiA+Ph55eXlF/pSKv78/nJ2dsW3bNo3tT6UE5Ouvv8bXX39dqnUlEgmuX7+uVlBERES6RJMXEFNXw4YNsWHDBoVlN27cwLx58zB79mw4OjoWuV1mZiaOHTtWbHtxVEpAWrdujXr16qm0AyIiIipZOcg/YGZmBjc3tyLbHBwc4ODggPPnz2P16tXo3Lkz6tSpgydPniA8PBxJSUlYunSpSvtTKQHp1asXunfvrtIOiIiI6MNQvXp15OXlYfHixXjx4gUqVaoEV1dXzJ49G05OTir1pdaVUImIiEhzysMQTFHc3Nzwzz//yO/XrVsXa9as0UjfTECIiIhEVk7zjzLF03CJiIhI60pdAbl582ZZxkFERKSzdLEawCEYIiIikenidbZ0MekiIiIikbECQkREJDLdq38wASEiIhJdeT0NtywxASEiIhKZ7qUfnANCREREImAFhIiISGQ6OALDBISIiEhsPA2XiIiISAtYASEiIhKZLlYDmIAQERGJjEMwRERERFrACggREZHIdK/+wQSEiIhIdByCISIiItICVkCIiIhEpovVACYgREREItPFIRgmIERERCLTvfRDN6s+REREJDJWQIiIiESmgyMwTECIiIjEpqeDgzAcgiEiIiKtYwWEiIhIZByCISIiIq2TcAiGiIiIqOwxASEiIhKZRKK5m6ZkZGSgffv2kMlkuHLlikLbr7/+Ci8vLzg6OqJHjx74448/VO6fCQgREZHI9CDR2E1Tli1bhvz8fKXl+/fvx7fffgtvb2+EhYXBxcUF48ePx6VLl1TqnwkIERERKbh79y42b96MCRMmKLUFBQXBx8cHkyZNQsuWLTFnzhw4OjoiNDRUpX0wASEiIhJZeRuCmTt3LgYMGID69esrLH/06BHu378Pb29vheXdunXD6dOnkZubW+p98CwYIiIikWly7kanTp1KbD9y5EiJ7dHR0bh16xaCg4Nx7do1hba4uDgAUEpM7OzskJeXh0ePHsHOzq5UcTIBISIiEll5OQ03KysL8+fPx+TJk2FiYqLUnpKSAgAwMzNTWF54v7C9NJiAEBERfUDeVuEoyfLly1G1alV88sknGoyoaExAiIiIRKZXDgogCQkJWLt2LUJDQ5GWlgYAyMzMlP8/IyMD5ubmAIC0tDRUr15dvm1qaioAyNtLgwkIERGRyMrDEEx8fDzy8vIwevRopTZ/f384Ozvj559/BvBqLoitra28PS4uDgYGBrC2ti71/piAEBERERo2bIgNGzYoLLtx4wbmzZuH2bNnw9HREdbW1qhXrx6io6Ph6ekpXy8qKgqtWrWCoaFhqffHBISIiEhk5eHH6MzMzODm5lZkm4ODAxwcHAAAEyZMwJQpU2BjYwM3NzdERUUhNjYWGzduVGl/TECIiIhEVh6GYErL19cXWVlZCAsLw6pVq1C/fn2EhITA1dVVpX4kgiAIZRRjuZaeo5OHTVSiCvrvz4cgkTYZlfHX9WP/PNNYXx1klhrrqyyxAkJl6trVK9i3ZyfOnzuLxwkJMLewgKOTMz4f/yXq1lO8kM3BA79j04Z1uH8/Dvp6erCz/xj+w0aiXfsO4gRPVEauXonFnt27cO5sDB4/ToCFuQWcnJ3xxcRJqPfa+8LZQVZsHy1btcbK1eHaCJe0oDycBaNtrIBQmfrmq4m4dOkiPLt44eOPZUhOfoptkZuQmZmJdRu3wP5jKQBgy+YI/DT/B7Rt74527TsiNycHe/fsxK1/buKnX4Lg4dlF5CPRDayAaMf/Jk3EpYt/o7NXV0ilMjx9moQtm1+9LyIit+Lj//++2Ld3t9K2169exaaNGzD5f19j6PCR2g5dZ5V1BeTkreca66udtIrG+ipLTECoTF2+9DcaOTSGgcH/zYx++OA++n/SA506e2HuvJ8AAL27e8HU1AzrN22D5P/PxkpPT4e3Z3s0b9ESvwQtEyV+XcMERDsuXfwbDg6NYfDaGQMPHtxH317d4dnFC/MWLCp22+8CZ2DXju04cPgYataqpY1wCUxAygKHYKhMObs0UVpmU7cebO3scS/urnxZRnoGbOrWkycfAGBiYoJKxpVR0chIK7ESaYuLq/L7om7derCz/xj3/v9vbRQlNzcXhw8dRNNmzZl8fGDKw1kw2sZfwyWtEwQBz5KTYVHl/7L0ps1b4PSpP7FlcwQeJ8Tj3r04zP9hDtLT0zBwkJ+I0RJphyAISE5+CguL4r+9njxxHGmpqfDx7aHFyEgbJBq8vS/euwrI8+fPcefOHTRv3lzsUEhNv+/fiydPEjH2i4nyZV9PnYEXz5/jp/k/4Kf5PwAALKpUwYqwcDg5q3ZqF9H7aP++PXiSmIjPx08sdp2ofXthaGgIzy5eWoyMtEFPB0sg710F5OzZs/D39xc7DFLTvXtxmP/jHDg5u8C3Ry/5cqNKRqhbrz58e/TCgkVLMGvOD6hWrTqmTJ6IRw8fiBcwkRbci7uLeXPnwNnFFT169i5ynfT0dJw8cQxt27sr/RIp0fvovauA0Pvr6dMkfPnFGJiYmGLhz0uhr68vb5v6v0nQ19fHkpAV8mXuHTuht68XQoOXYP5Pi8UImajMPU1KwvjPX70vFi1WfF+87vChA8jJyYGPT3ctR0jaoHv1j3KUgHTvXro3VUZGRhlHQmUhLS0NEz8fjfS0VKxetwnVa9SUt8XHP8Jfp05iRuAchW3MzS3g4toUly/+re1wibQiLS0Nn48dhbTUNIRv2IQar70v3hS1by9MTU3RvkNHLUZIWqODGUi5SUDi4uJgb2+PRo0albheQkIC/v33Xy1FRZqQk5ODyRPG4cH9+1getha2dvYK7c+SnwIACgoKlLZ9+fIl8vPztRInkTbl5ORg4hdj8eDBfaxaHQ47e/ti101KeoJzZ2PQo1dvlX7si6g8KzcJyMcff4y6deti3rx5Ja534MABnDt3TktR0bvKz8/HtK8nIzb2En5ZGlrkhFJr67rQ09PDwQNR+OTT/vJTcRP/+w8X/z4PF9em2g6bqEzl5+fjm/9NQuzlS1gSvAzOLiVPtI6OikJBQQG6cfjlg/U+/RaMppSbBMTJyQknT54s1bo6eu2099LiRQtw/NhRtHfviNSUFETt26PQ3s23B6pYWqJHr0+wa8evGDtyKDw8OyMjIwO/bY1ETk4Oho0YLVL0RGXj54XzceyPo3Dv0BEpKS+Urnjq272nwv2o/XtQvUYNNG9R9C+V0vtPB0+CKT9XQn348CFu376NTp06lbhednY2kpOTUadOnXfaH6+Eqh2jh/vhwvniK1YXYm8CeDXUsv3XLdi9c7v8rJdGDo4YOWYcmrdoqZVYiVdC1ZYRQ/1w/tzZYtsvX/tH/u/79+LQ09cbfkOGYco3AdoIj4pQ1ldCPRuXorG+Wtiaa6yvslRuEhBtYwJCpIwJCFHRyjoBOafBBKT5e5KAlJshGCIiIp2lg7n/e3chMiIiInr/sQJCREQkMp4FQ0RERFqni2fBMAEhIiISmQ7mH5wDQkRERNrHCggREZHYdLAEwgSEiIhIZLo4CZVDMERERKR1rIAQERGJjGfBEBERkdbpYP7BIRgiIiLSPlZAiIiIxKaDJRAmIERERCLTxbNgmIAQERERjh8/jrCwMNy5cwfp6emoWbMmPD09MX78eJiamgIAAgICsHPnTqVtw8LC0L59e5X2xwSEiIhIZOXhLJgXL17AyckJfn5+sLCwwO3btxEcHIzbt29j7dq18vWsra2xaNEihW3t7OxU3h8TECIiIpGVg/wDPXv2VLjv5uYGQ0NDfPvtt0hMTETNmjUBAEZGRnBxcXnn/TEBISIiElt5yECKYGFhAQDIy8vTeN88DZeIiIjk8vPzkZOTg2vXriE0NBQeHh6wsrKStz948ABNmzZF48aN0adPHxw+fFit/bACQkREJDJNngXTqVOnEtuPHDlSYnvHjh2RmJgIAGjXrh1+/vlneVvDhg3h6OgIe3t7pKWlITIyEl988QWWLl2Krl27qhSnRBAEQaUtPhDpOTp52EQlqqBfTuvARCIzKuOv69cfZ2isrwl+PUpsf1sCcvPmTWRlZeHOnTtYvnw5rKysEB4eDn19faV1CwoKMGDAAKSnpyMqKkqlOFkBISIi+oC8LcF4mwYNGgAAXF1d4ejoiJ49e+LQoUNFVjj09PTQpUsX/PTTT8jOzoaRkVGp98MEhIiISGTltfYok8lgYGCAhw8farxvJiBERERiK6cZyOXLl5GXl6cwCfV1BQUFiI6Oxscff6xS9QNgAkJEREQAxo8fj8aNG0Mmk8HIyAg3b97EmjVrIJPJ4OnpiYSEBAQEBMDHxwd169ZFSkoKIiMjcfXqVQQHB6u8PyYgREREIisPvwXj5OSEqKgorFq1CoIgoE6dOvj0008xYsQIGBoaonLlyjAxMcHy5cuRnJwMAwMDNG7cGGFhYWjXrp3K++NZMEQkx7NgiIpW1mfB/PNfpsb6ktUy1lhfZYkXIiMiIiKt4xAMERGRyHSx9sgEhIiISGw6mIEwASEiIhJZeZiEqm2cA0JERERaxwoIERGRyCS6VwBhAkJERCQ2Hcw/OARDRERE2scKCBERkdh0sATCBISIiEhkPAuGiIiISAtYASEiIhIZz4IhIiIirdPB/IMJCBERkeh0MAPhHBAiIiLSOlZAiIiIRKaLZ8EwASEiIhKZLk5C5RAMERERaR0rIERERCLTwQIIExAiIiKxcQiGiIiISAtYASEiIhKd7pVAmIAQERGJjEMwRERERFrACggREZHIdLAAwgSEiIhIbLo4BMMEhIiISGS6eCl2zgEhIiIirWMCQkREJDaJBm9qOn78OAYPHoyWLVuicePG6NSpE+bNm4e0tDSF9Y4ePYoePXrA0dERXl5e2L59u1r74xAMERGRyMrDAMyLFy/g5OQEPz8/WFhY4Pbt2wgODsbt27exdu1aAMD58+cxfvx49O3bF9OnT8eZM2cwY8YMVK5cGV27dlVpfxJBEISyOJDyLj1HJw+bqEQV9MvDxyBR+WNUxl/XE1PzNNZXTTMDjfW1bds2fPvttzhx4gRq1qyJESNGICMjA1u2bJGv87///Q83btxAVFSUSn1zCIaIiEhkEonmbppkYWEBAMjLy0Nubi5iYmKUKh3dunXD3bt3ER8fr1LfTECIiIhEJtHgf+8qPz8fOTk5uHbtGkJDQ+Hh4QErKys8fPgQeXl5sLW1VVjfzs4OABAXF6fSfjgHhIiI6APSqVOnEtuPHDlSYnvHjh2RmJgIAGjXrh1+/vlnAEBKSgoAwMzMTGH9wvuF7aXFBISIiEhs5Wj61apVq5CVlYU7d+5g+fLlGDt2LMLDwzW+HyYgREREItNk/vG2CsfbNGjQAADg6uoKR0dH9OzZE4cOHYK9vT0AKJ2Wm5qaCgAwNzdXaT+cA0JERERFkslkMDAwwMOHD2FjYwMDAwOluR6F99+cG/I2TECIiIhEVl7Pgrl8+TLy8vJgZWUFQ0NDuLm54cCBAwrrREVFwc7ODlZWVir1zSEYIiIikZWH34IZP348GjduDJlMBiMjI9y8eRNr1qyBTCaDp6cnAGDcuHHw9/fHd999B29vb8TExGDfvn1YvHixyvvjhciISI4XIiMqWllfiOx5Zr7G+qpirK/WdqtWrUJUVBQePnwIQRBQp04ddO7cGSNGjICJiYl8vSNHjmDJkiW4d+8eateujdGjR6Nv374q748JCBHJMQEhKpouJCDaxjkgREREpHWcA0JERCQyTU8efR+wAkJERERaxwoIERGRyMrDWTDaxgSEiIhIZByCISIiItICVkCIiIhEpoMFECYgREREotPBDIRDMERERKR1rIAQERGJjGfBEBERkdbp4lkwTECIiIhEpoP5B+eAEBERkfaxAkJERCQ2HSyBMAEhIiISmS5OQuUQDBEREWkdKyBEREQi08WzYCSCIAhiB0FERES6hUMwREREpHVMQIiIiEjrmIAQERGR1jEBISIiIq1jAkJERERaxwSEiIiItI4JCBEREWkdExAiIiLSOiYgREREpHVMQIiIiEjrmIAQERGR1jEBISIiIq1jAkJERERaxwSERHH37l0MGzYMLi4uaNOmDRYuXIjc3FyxwyIS1YMHDxAYGIiePXuiUaNG8PX1FTskojJTQewASPekpKRgyJAhqFevHoKDg5GYmIj58+cjOzsbgYGBYodHJJrbt2/j+PHjcHZ2RkFBAQRBEDskojLDBIS0bsuWLcjIyEBISAgsLCwAAPn5+Zg9ezbGjBmDmjVrihsgkUg8PDzg6ekJAAgICMDVq1dFjoio7HAIhrTuxIkTaNWqlTz5AABvb28UFBTg1KlT4gVGJDI9PX4kk+7gq520Li4uDra2tgrLzMzMUL16dcTFxYkUFRERaRMTENK61NRUmJmZKS03NzdHSkqKCBEREZG2MQEhIiIirWMCQlpnZmaGtLQ0peUpKSkwNzcXISIiItI2JiCkdba2tkpzPdLS0pCUlKQ0N4SIiD5MTEBI69q3b4+//voLqamp8mXR0dHQ09NDmzZtRIyMiIi0hdcBIa0bMGAAIiIi8MUXX2DMmDFITEzEwoULMWDAAF4DhHRaVlYWjh8/DgBISEhAeno6oqOjAQAtWrSApaWlmOERaZRE4KX2SAR3797F999/j4sXL6Jy5cro2bMnJk+eDENDQ7FDIxJNfHw8OnXqVGTbhg0b4ObmpuWIiMoOExAiIiLSOs4BISIiIq1jAkJERERaxwSEiIiItI4JCBEREWkdExAiIiLSOiYgREREpHVMQIiIiEjrmIAQERGR1jEBISpHPDw8EBAQIL8fExMDmUyGmJgYEaNS9GaMxZHJZAgODla5/x07dkAmk+HKlSvqhFek4OBgyGQyjfVHRO+OCQjR/1f4h6/w5ujoCC8vL8yZMwdPnz4VOzyVHD9+XK0//kRE2sIfoyN6w8SJE2FlZYXc3FxcuHABkZGROH78OPbt24dKlSppNZbmzZsjNjYWBgYGKm13/PhxbNq0CRMmTCijyIiI3g0TEKI3tG/fHo6OjgCATz/9FBYWFggPD8eRI0fg6+tb5DaZmZkwNjbWeCx6enqoWLGixvslIhIbh2CI3qJly5YAXv1SKQAEBATA1dUVDx8+xKhRo+Dq6oopU6YAAAoKCrBu3Tr4+PjA0dERrVu3RmBgIFJSUhT6FAQBy5YtQ/v27eHs7Aw/Pz/cvn1bad/FzQG5fPkyRo0ahebNm8PFxQXdu3fH+vXr5fFt2rQJABSGlAppOsbSSkhIwHfffQcvLy84OTnBzc0NEydOlD+ub8rOzkZgYCDc3NzQpEkTfPPNN0oxAq+qPZ999hlcXFzg6uqK0aNHlyrOU6dOYeDAgWjWrBlcXV3h5eWFX375Re3jIyLVsAJC9BYPHz4EAFhYWMiXvXz5EiNGjEDTpk0xdepUGBkZAQACAwOxc+dO9OnTB35+foiPj8emTZtw/fp1REZGyodSli5diuXLl8Pd3R3u7u64du0ahg8fjry8vLfGc+rUKYwZMwY1atSAv78/qlWrhrt37+LYsWMYMmQI+vfvjydPnuDUqVNYuHCh0vbaiLEoV65cwcWLF+Hj44NatWohISEBkZGR8Pf3x/79+5WGt+bMmQMzMzOMHz8e9+7dQ2RkJB4/foyIiAhIJBIAwK5duxAQEIC2bdtiypQpyMrKQmRkJD777DPs3LkTVlZWRcZy+/ZtjBkzBjKZDBMnToShoSEePHiAv//+W61jIyI1CEQkCIIgbN++XZBKpcJff/0lJCcnC//++6+wf/9+oUWLFoKTk5Pw33//CYIgCFOnThWkUqmwaNEihe3PnTsnSKVSYc+ePQrLT5w4obA8OTlZcHBwEEaPHi0UFBTI1/vll18EqVQqTJ06Vb7szJkzglQqFc6cOSMIgiC8fPlS8PDwEDp27CikpKQo7Of1vmbPni1IpVKlYyyLGIsjlUqFoKAg+f2srCyldS5evChIpVJh586d8mWFz0Pv3r2F3Nxc+fKwsDBBKpUKhw8fFgRBENLT04VmzZoJM2fOVOgzKSlJaNq0qcLyoKAghccjPDxckEqlQnJy8luPg4jKBodgiN4wdOhQtGrVCu7u7pg8eTIqV66MkJAQ1KxZU2G9gQMHKtyPjo6Gqakp2rRpg2fPnslvDg4OMDY2lg+j/PXXX8jLy8PgwYPl3+QBYMiQIW+N7fr164iPj4e/vz/MzMwU2l7vqzjaiLE4hVUiAMjLy8Pz589hY2MDMzMzXL9+XWn9/v37K0y+HThwICpUqIDjx4/LY0xNTYWPj4/Csejp6cHZ2bnEU5cLH7sjR46goKBA7WMiIvVxCIboDYGBgahfvz709fVRrVo11K9fH3p6irl6hQoVUKtWLYVlDx48QFpaGlq1alVkv8nJyQCAx48fAwDq1aun0G5paQlzc/MSY3v06BEAQCqVlvp4tB1jcbKzs7Fy5Urs2LEDiYmJEARB3paWlqa0ft26dRXuV65cGdWrV0dCQgIA4P79+wCKT4pMTEyKjaVbt2749ddfMXPmTPz8889o1aoVOnfujK5duyo910RUNpiAEL3ByclJfhZMcQwNDZX+UBUUFKBq1apYtGhRkdtYWlpqLEZ1iRnj999/jx07dmDIkCFwcXGBqakpJBIJJk+erJCMlFbhNgsXLkT16tWV2vX19Yvd1sjICJs2bUJMTAyOHTuGkydPIioqClu3bsXatWtL3JaINIMJCJGG2NjY4PTp02jSpInCcMObateuDeDVN3hra2v58mfPnhV5lsfrCte/desWWrduXex6xQ3HaCPG4hw4cAC9evVSuIpqTk5OkdUP4FW1pvAMJADIyMhAUlIS2rdvD+D/HouqVauW+FgUR09PD61atUKrVq0wbdo0rFixAosXL0ZMTIxa/RGRalhrJNIQb29v5OfnY9myZUptL1++RGpqKgCgdevWMDAwwMaNGxW++ReeRlsSBwcHWFlZYcOGDfL+Cr3eV+EZJW+uo40Yi1NUVSEiIgL5+flFrr9161aFM24iIyPx8uVLeQLSrl07mJiYYOXKlUWemfPs2bNiY3nx4oXSsoYNGwIAcnNzSzwOItIMVkCINKRFixbo378/Vq5ciRs3bqBNmzYwMDDA/fv3ER0djRkzZqBr166wtLTE8OHDsXLlSowZMwbu7u64fv06Tpw4gSpVqpS4Dz09PXz33XcYN24cevXqhT59+qB69eqIi4vDnTt3sGbNGgCvEhUAmDt3Ltq2bQt9fX34+PhoJcbidOjQAbt374aJiQns7e1x6dIl/PXXXwqnN78uLy8PQ4cOhbe3N+7du4fNmzejadOm6NSpE4BXczy+++47fPPNN+jTpw+6desGS0tLPH78GMePH0eTJk0QGBhYZN+hoaE4f/483N3dUadOHSQnJ2Pz5s2oVasWmjZtqtbxEZFqmIAQadCcOXPQuHFjbNmyBYsXL4a+vj7q1KmDHj16oEmTJvL1Jk2aBENDQ2zZsgUxMTFwcnLC2rVrMWbMmLfuo127dli/fj1CQ0Oxdu1aCIIAa2tr9OvXT75Oly5d4Ofnh/3792PPnj0QBAE+Pj5ai7EoM2bMgJ6eHvbu3YucnBw0adIE4eHhGDlyZJHrBwYGYu/evQgKCkJeXh58fHwwc+ZMheGl7t27o0aNGli1ahXWrFmD3Nxc1KxZE82aNUOfPn2KjcXDwwMJCQnYvn07nj9/jipVqqBFixaYMGECTE1N1To+IlKNRFBn9hcRERHRO+AcECIiItI6JiBERESkdUxAiIiISOuYgBAREZHWMQEhIiIirWMCQkRERFrHBISIiIi0jgkIERERaR0TECIiItI6JiBERESkdUxAiIiISOuYgBAREZHW/T8PY1Q36E/LsgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(ANN_cm, \"Artificial Neural Network Classifier Confusion Matrix\")"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "gpuType": "T4",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
